AI Governance
428 records. Direct records match this topic; context records explain their connections.
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control · Context
A001 — Establish input data policy
Establish input data policy
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A002 — Establish output data policy
Establish output data policy
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A006 — Prevent PII leakage
Prevent PII leakage
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A007 — Prevent IP violations
Prevent IP violations
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A008 — Prevent leakage of credentials and secrets
Prevent leakage of credentials and secrets
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B001 — Third-party testing of adversarial robustness
Third-party testing of adversarial robustness
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B002 — Detect adversarial input
Detect adversarial input
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B003 — Manage public release of technical details
Manage public release of technical details
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B004 — Prevent AI endpoint scraping
Prevent AI endpoint scraping
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B005 — Implement real-time input filtering
Implement real-time input filtering
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B006 — Prevent unauthorized AI agent actions
Prevent unauthorized AI agent actions
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B008 — Protect AI system deployment environment
Protect AI system deployment environment
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B009 — Limit output over-exposure
Limit output over-exposure
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B010 — Promote secure patterns in generated code
Promote secure patterns in generated code
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C001 — Define AI risk taxonomy
Define AI risk taxonomy
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C002 — Conduct pre-deployment testing
Conduct pre-deployment testing
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C003 — Prevent harmful outputs
Prevent harmful outputs
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C004 — Prevent out-of-scope outputs
Prevent out-of-scope outputs
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C005 — Prevent agent-specific high risk outputs
Prevent agent-specific high risk outputs
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C006 — Prevent output vulnerabilities
Prevent output vulnerabilities
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C007 — Flag high risk outputs for human review
Flag high risk outputs for human review
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C008 — Monitor AI risk categories
Monitor AI risk categories
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C009 — Enable real-time feedback and intervention
Enable real-time feedback and intervention
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C010 — Third-party testing for harmful outputs
Third-party testing for harmful outputs
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C011 — Third-party testing for out-of-scope outputs
Third-party testing for out-of-scope outputs
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C012 — Third-party testing for customer-defined risk
Third-party testing for customer-defined risk
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D001 — Prevent hallucinated outputs
Prevent hallucinated outputs
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D002 — Third-party testing for hallucinations
Third-party testing for hallucinations
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D003 — Restrict unsafe tool calls
Restrict unsafe tool calls
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D004 — Third-party testing of tool calls
Third-party testing of tool calls
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E001 — AI failure plan for security breaches
AI failure plan for security breaches
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E002 — AI failure plan for harmful outputs
AI failure plan for harmful outputs
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E003 — AI failure plan for hallucinations
AI failure plan for hallucinations
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E004 — Assign accountability
Assign accountability
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E006 — Conduct vendor due diligence
Conduct vendor due diligence
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E008 — Review internal processes
Review internal processes
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E010 — Establish AI acceptable use policy
Establish AI acceptable use policy
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E012 — Document regulatory compliance
Document regulatory compliance
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E013 — Implement quality management system
Implement quality management system
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E015 — Log AI system activity
Log AI system activity
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E016 — Implement AI disclosure mechanisms
Implement AI disclosure mechanisms
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E017 — Document system transparency policy
Document system transparency policy
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F001 — Prevent AI cyber misuse
Prevent AI cyber misuse
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F002 — Prevent catastrophic misuse
Prevent catastrophic misuse
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CCPA-1798.100 — Notice at collection and consumer right to know
Notice at collection and consumer right to know
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CCPA-1798.105 — Right to delete personal information
Right to delete personal information
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CCPA-1798.106 — Right to correct inaccurate personal information
Right to correct inaccurate personal information
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CCPA-1798.110-115 — Rights to access and disclosure of personal information collected, sold, or shared
Rights to access and disclosure of personal information collected, sold, or shared
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CCPA-1798.120-121 — Right to opt out of sale/sharing and to limit use of sensitive personal information
Right to opt out of sale/sharing and to limit use of sensitive personal information
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CCPA-1798.125 — Non-discrimination and financial-incentive requirements
Non-discrimination and financial-incentive requirements
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CCPA-1798.130-135 — Request-handling mechanics, verification, and opt-out link requirements
Request-handling mechanics, verification, and opt-out link requirements
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CCPA-1798.185 — CPPA regulations: cybersecurity audits and risk assessments
CPPA regulations: cybersecurity audits and risk assessments
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APO09 — Managed Service Agreements
Managed Service Agreements
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APO10 — Managed Vendors
Managed Vendors
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BAI03 — Managed Solutions Identification and Build
Managed Solutions Identification and Build
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BAI06 — Managed IT Changes
Managed IT Changes
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BAI07 — Managed IT Change Acceptance and Transitioning
Managed IT Change Acceptance and Transitioning
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BAI09 — Managed Assets
Managed Assets
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BAI10 — Managed Configuration
Managed Configuration
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MEA04 — Managed Assurance
Managed Assurance
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E15 — Assesses Substantial Change
Assesses Substantial Change
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E16 — Reviews Risk and Performance
Reviews Risk and Performance
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E17 — Pursues Improvement in Enterprise Risk Management
Pursues Improvement in Enterprise Risk Management
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P9 — The organization identifies and assesses changes that could significantly impact the system of internal control.
The organization identifies and assesses changes that could significantly impact the system of internal control.
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DORA-Art28-44 — Managing of ICT third-party risk
Managing of ICT third-party risk
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AIA-Art10 — Data and data governance (high-risk)
Data and data governance (high-risk)
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AIA-Art11 — Technical documentation (high-risk)
Technical documentation (high-risk)
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AIA-Art12 — Record-keeping / logging (high-risk)
Record-keeping / logging (high-risk)
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AIA-Art13 — Transparency and provision of information to deployers (high-risk)
Transparency and provision of information to deployers (high-risk)
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AIA-Art14 — Human oversight (high-risk)
Human oversight (high-risk)
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AIA-Art15 — Accuracy, robustness and cybersecurity (high-risk)
Accuracy, robustness and cybersecurity (high-risk)
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AIA-Art16 — Obligations of providers of high-risk AI systems
Obligations of providers of high-risk AI systems
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AIA-Art17 — Quality management system (providers of high-risk AI systems)
Quality management system (providers of high-risk AI systems)
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AIA-Art18 — Documentation keeping — 10-year retention of technical documentation, QMS records and conformity documents (providers)
Documentation keeping — 10-year retention of technical documentation, QMS records and conformity documents (providers)
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AIA-Art19 — Automatically generated logs — provider retention of high-risk system logs (minimum six months)
Automatically generated logs — provider retention of high-risk system logs (minimum six months)
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AIA-Art20 — Corrective actions and duty of information for non-conforming high-risk AI systems
Corrective actions and duty of information for non-conforming high-risk AI systems
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AIA-Art21-22 — Cooperation with competent authorities; authorised representatives of non-EU providers
Cooperation with competent authorities; authorised representatives of non-EU providers
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AIA-Art23-25 — Obligations of importers and distributors; responsibilities along the AI value chain
Obligations of importers and distributors; responsibilities along the AI value chain
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AIA-Art26 — Obligations of deployers of high-risk AI systems
Obligations of deployers of high-risk AI systems
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AIA-Art27 — Fundamental rights impact assessment for high-risk AI systems (deployers)
Fundamental rights impact assessment for high-risk AI systems (deployers)
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AIA-Art43 — Conformity assessment of high-risk AI systems
Conformity assessment of high-risk AI systems
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AIA-Art47-49 — EU declaration of conformity, CE marking and registration in the EU database
EU declaration of conformity, CE marking and registration in the EU database
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AIA-Art5 — Prohibited AI practices
Prohibited AI practices
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AIA-Art50 — Transparency obligations for certain AI systems (deepfakes, chatbots, emotion recognition)
Transparency obligations for certain AI systems (deepfakes, chatbots, emotion recognition)
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AIA-Art53 — Obligations for providers of general-purpose AI (GPAI) models
Obligations for providers of general-purpose AI (GPAI) models
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AIA-Art55 — Obligations for GPAI models with systemic risk
Obligations for GPAI models with systemic risk
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AIA-Art6-7 — Risk-based classification of high-risk AI systems
Risk-based classification of high-risk AI systems
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AIA-Art72 — Post-market monitoring by providers of high-risk AI systems
Post-market monitoring by providers of high-risk AI systems
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AIA-Art73 — Reporting of serious incidents (providers; deployers inform providers)
Reporting of serious incidents (providers; deployers inform providers)
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AIA-Art9 — Risk management system (high-risk)
Risk management system (high-risk)
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GDPR-Art12-14 — Transparency and information to data subjects
Transparency and information to data subjects
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GDPR-Art15-22 — Data subject rights (access, rectification, erasure, portability, objection, automated decisions)
Data subject rights (access, rectification, erasure, portability, objection, automated decisions)
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GDPR-Art7 — Conditions for consent
Conditions for consent
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GDPR-Art9 — Processing of special categories of data
Processing of special categories of data
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HIPAA-164.508 — Authorizations required for other uses and disclosures of PHI
Authorizations required for other uses and disclosures of PHI
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HIPAA-164.514 — De-identification of PHI and limited data sets
De-identification of PHI and limited data sets
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HIPAA-164.520 — Notice of privacy practices for PHI
Notice of privacy practices for PHI
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HIPAA-164.524 — Individual right of access to PHI
Individual right of access to PHI
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HIPAA-164.526 — Individual right to amend PHI
Individual right to amend PHI
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Principle 3 — Demonstrate Competency
Demonstrate Competency
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Principle 5 — Maintain Confidentiality
Maintain Confidentiality
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Principle 8 — Overseen by the Board
Overseen by the Board
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Std 3.1 — Competency
Competency
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Std 3.2 — Continuing Professional Development
Continuing Professional Development
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Std 5.1 — Use of Information
Use of Information
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Std 5.2 — Protection of Information
Protection of Information
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Std 8.1 — Board Interaction
Board Interaction
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Std 8.2 — Resources
Resources
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Std 9.5 — Coordination and Reliance
Coordination and Reliance
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IIA-POS-TLM-03 — Assurance Coordination and Reliance
Assurance providers coordinate and rely on one another only where independence, competence, evidence, and coverage support reliance; gaps and duplication remain visible to the board.
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A.5.19 — Information security in supplier relationships
Information security in supplier relationships
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A.5.21 — Managing information security in the ICT supply chain
Managing information security in the ICT supply chain
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A.5.23 — Information security for use of cloud services
Information security for use of cloud services
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A.5.35 — Independent review of information security
Independent review of information security
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A.8.11 — Data masking
Data masking
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A.8.27 — Secure system architecture and engineering principles
Secure system architecture and engineering principles
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A.8.28 — Secure coding
Secure coding
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A.8.29 — Security testing in development and acceptance
Security testing in development and acceptance
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A.8.30 — Outsourced development
Outsourced development
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A.8.32 — Change management
Change management
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31000-FW1 — Leadership and commitment
Leadership and commitment
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31000-FW3 — Design
Design
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31000-FW4 — Implementation
Implementation
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31000-FW5 — Evaluation
Evaluation
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31000-FW6 — Improvement
Improvement
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31000-P3 — Customized
Customized
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31000-P5 — Dynamic
Dynamic
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31000-P8 — Continual improvement
Continual improvement
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31000-PR7 — Monitoring and review
Monitoring and review
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A.10.2 — Allocating responsibilities
Allocating responsibilities
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A.10.3 — Suppliers
Suppliers
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A.10.4 — Customers
Customers
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A.2.2 — AI policy
AI policy
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A.2.3 — Alignment with other organizational policies
Alignment with other organizational policies
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A.2.4 — Review of the AI policy
Review of the AI policy
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A.3.2 — AI roles and responsibilities
AI roles and responsibilities
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A.3.3 — Reporting of concerns
Reporting of concerns
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A.4.2 — Resource documentation
Resource documentation
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A.4.3 — Data resources
Data resources
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A.4.4 — Tooling resources
Tooling resources
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A.4.5 — System and computing resources
System and computing resources
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A.4.6 — Human resources
Human resources
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A.5.2 — AI system impact assessment process
AI system impact assessment process
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A.5.3 — Documentation of AI system impact assessments
Documentation of AI system impact assessments
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A.5.4 — Assessing AI system impact on individuals or groups of individuals
Assessing AI system impact on individuals or groups of individuals
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A.5.5 — Assessing societal impacts of AI systems
Assessing societal impacts of AI systems
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A.6.1.2 — Objectives for responsible development of AI systems
Objectives for responsible development of AI systems
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A.6.1.3 — Processes for responsible AI system design and development
Processes for responsible AI system design and development
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A.6.2.2 — AI system requirements and specification
AI system requirements and specification
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A.6.2.3 — Documentation of AI system design and development
Documentation of AI system design and development
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A.6.2.4 — AI system verification and validation
AI system verification and validation
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A.6.2.5 — AI system deployment
AI system deployment
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A.6.2.6 — AI system operation and monitoring
AI system operation and monitoring
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A.6.2.7 — AI system technical documentation
AI system technical documentation
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A.6.2.8 — AI system recording of event logs
AI system recording of event logs
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A.7.2 — Data for development and enhancement of AI systems
Data for development and enhancement of AI systems
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A.7.3 — Acquisition of data
Acquisition of data
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A.7.4 — Data quality for AI systems
Data quality for AI systems
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A.7.5 — Data provenance
Data provenance
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A.7.6 — Data preparation
Data preparation
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A.8.2 — System documentation and information for users
System documentation and information for users
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A.8.3 — External reporting
External reporting
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A.8.4 — Communication of incidents
Communication of incidents
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A.8.5 — Information for interested parties
Information for interested parties
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A.9.2 — Processes for responsible use of AI systems
Processes for responsible use of AI systems
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A.9.3 — Objectives for responsible use of AI systems
Objectives for responsible use of AI systems
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A.9.4 — Intended use of the AI system
Intended use of the AI system
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NIS2-Art21d — Supply chain security
Supply chain security
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AC-14 — Permitted Actions Without Identification or Authentication
Permitted Actions Without Identification or Authentication
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AC-21 — Information Sharing
Information Sharing
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AC-22 — Publicly Accessible Content
Publicly Accessible Content
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CM-3 — Configuration Change Control
Configuration Change Control
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CM-4 — Impact Analyses
Impact Analyses
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PM-17 — Protecting Controlled Unclassified Information on External Systems
Protecting Controlled Unclassified Information on External Systems
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PM-30 — Supply Chain Risk Management Strategy
Supply Chain Risk Management Strategy
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PT-4 — Consent
Consent
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PT-5 — Privacy Notice
Privacy Notice
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PT-6 — System of Records Notice
System of Records Notice
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PT-7 — Specific Categories of Personally Identifiable Information
Specific Categories of Personally Identifiable Information
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PT-8 — Computer Matching Requirements
Computer Matching Requirements
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SA-10 — Developer Configuration Management
Developer Configuration Management
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SA-11 — Developer Testing and Evaluation
Developer Testing and Evaluation
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SA-17 — Developer Security and Privacy Architecture and Design
Developer Security and Privacy Architecture and Design
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SA-20 — Customized Development of Critical Components
Customized Development of Critical Components
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SA-21 — Developer Screening
Developer Screening
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SA-22 — Unsupported System Components
Unsupported System Components
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SA-23 — Specialization
Specialization
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SA-8 — Security and Privacy Engineering Principles
Security and Privacy Engineering Principles
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SA-9 — External System Services
External System Services
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SC-39 — Process Isolation
Process Isolation
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SI-10 — Information Input Validation
Information Input Validation
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SI-14 — Non-persistence
Non-persistence
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SI-18 — Personally Identifiable Information Quality Operations
Personally Identifiable Information Quality Operations
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SI-19 — De-identification
De-identification
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SI-21 — Information Refresh
Information Refresh
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SI-22 — Information Diversity
Information Diversity
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SI-23 — Information Fragmentation
Information Fragmentation
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SR-1 — Policy and Procedures
Policy and Procedures
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SR-10 — Inspection of Systems or Components
Inspection of Systems or Components
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SR-11 — Component Authenticity
Component Authenticity
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SR-2 — Supply Chain Risk Management Plan
Supply Chain Risk Management Plan
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SR-3 — Supply Chain Controls and Processes
Supply Chain Controls and Processes
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SR-4 — Provenance
Provenance
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SR-5 — Acquisition Strategies, Tools, and Methods
Acquisition Strategies, Tools, and Methods
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SR-9 — Tamper Resistance and Detection
Tamper Resistance and Detection
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NIST-AGI-03 — Context-sensitive authorization and least privilege
The paper asks how zero trust, changing agent context, aggregated data sensitivity, least privilege, and proof of authority for a specific action can inform agent authorization.
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NIST-AGI-04 — Delegated authority and human accountability
The project explores linking agents to the users on whose behalf they act, with delegation controls and a binding between agent identity and human authorization.
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NIST-AGI-05 — Verifiable agent action logs and authorization traceability
The paper asks how agent actions and intent can be logged in a verifiable, tamper-resistant way and traced back to human authorization; visibility includes actions, data, and outcomes.
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NIST-AGI-06 — Prompt-injection prevention and limits on resulting harm
The paper explicitly asks which controls help prevent direct and indirect prompt injection and which controls can limit its impact after an injection succeeds.
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NIST-AGI-07 — Prompt provenance and data-flow tracking
The proposed project explores tracking the provenance of user prompts and input data so that risk assessments and policies can take those sources into account when deciding whether an agent may act.
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NIST-TEVV-01 — Define evaluation objectives, context, and measurements
The framework starts with organizational goals and the system's operational context, then constructs measurement concepts, test events, and tools that address those objectives.
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NIST-TEVV-02 — Run evaluations and examine results and limitations
The framework applies the selected tests and synthesizes their results into evidence about system performance, while interrogating the measurements and what the findings support.
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NIST-TEVV-03 — Evaluate AI systems in realistic operating settings
The draft discusses testing in settings that better reflect real use, including interactions with users and the operating environment, to complement model tests and benchmarks.
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NIST-TEVV-04 — Test for disclosure of confidential information
Appendix B includes confidentiality attacks that test whether an AI system reveals confidential information or internal functionality, including user information and system-prompt leakage.
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NIST-TEVV-05 — Test direct and indirect prompt injection
Appendix B includes integrity tests for direct and indirect prompt injection, poisoning, obfuscated inputs, and retrieval weaknesses that could alter intended outputs or outcomes.
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NIST-TEVV-06 — Test agent tool misuse and unauthorized external actions
Appendix B includes tests for misuse of connected tools and external actions, including unsafe tool selection, excessive agency, unauthorized action attempts, and harmful task execution.
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GV.SC-01 — Cybersecurity Supply Chain Risk Management: A cybersecurity supply chain risk management program, strategy, objectives, policies, and processes are established and agreed to by organizational stakeholders
Cybersecurity Supply Chain Risk Management: A cybersecurity supply chain risk management program, strategy, objectives, policies, and processes are established and agreed to by organizational stakeholders
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GV.SC-02 — Cybersecurity Supply Chain Risk Management: Cybersecurity roles and responsibilities for suppliers, customers, and partners are established, communicated, and coordinated internally and externally
Cybersecurity Supply Chain Risk Management: Cybersecurity roles and responsibilities for suppliers, customers, and partners are established, communicated, and coordinated internally and externally
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GV.SC-03 — Cybersecurity Supply Chain Risk Management: Cybersecurity supply chain risk management is integrated into cybersecurity and enterprise risk management, risk assessment, and improvement processes
Cybersecurity Supply Chain Risk Management: Cybersecurity supply chain risk management is integrated into cybersecurity and enterprise risk management, risk assessment, and improvement processes
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GV.SC-04 — Cybersecurity Supply Chain Risk Management: Suppliers are known and prioritized by criticality
Cybersecurity Supply Chain Risk Management: Suppliers are known and prioritized by criticality
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GV.SC-06 — Cybersecurity Supply Chain Risk Management: Planning and due diligence are performed to reduce risks before entering into formal supplier or other third-party relationships
Cybersecurity Supply Chain Risk Management: Planning and due diligence are performed to reduce risks before entering into formal supplier or other third-party relationships
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ID.IM-01 — Improvement: Improvements are identified from evaluations
Improvement: Improvements are identified from evaluations
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500.11 — Third-party service provider security policy
Third-party service provider security policy
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SOC1-2 — Change management — controls provide reasonable assurance that changes to applications and infrastructure are authorized, tested, approved, and migrated to production appropriately.
Change management — controls provide reasonable assurance that changes to applications and infrastructure are authorized, tested, approved, and migrated to production appropriately.
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SOC1-3 — Program development / SDLC — controls provide reasonable assurance that new systems and applications are developed, tested, approved, and implemented in accordance with management's intent.
Program development / SDLC — controls provide reasonable assurance that new systems and applications are developed, tested, approved, and implemented in accordance with management's intent.
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CC3.4 — The entity identifies and assesses changes that could significantly impact the system of internal control.
The entity identifies and assesses changes that could significantly impact the system of internal control.
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CC4.1 — The entity selects, develops, and performs ongoing and/or separate evaluations to ascertain whether the components of internal control are present and functioning.
The entity selects, develops, and performs ongoing and/or separate evaluations to ascertain whether the components of internal control are present and functioning.
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CC8.1 — The entity authorizes, designs, develops or acquires, configures, documents, tests, approves, and implements changes to infrastructure, data, software, and procedures to meet its objectives.
The entity authorizes, designs, develops or acquires, configures, documents, tests, approves, and implements changes to infrastructure, data, software, and procedures to meet its objectives.
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CC9.2 — The entity assesses and manages risks associated with vendors and business partners.
The entity assesses and manages risks associated with vendors and business partners.
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P1.1 — The entity provides notice to data subjects about its privacy practices to meet the entity's objectives related to privacy. The notice is updated and communicated to data subjects in a timely manner for changes to the entity's privacy practices, including changes in the use of personal information, to meet the entity's objectives related to privacy.
The entity provides notice to data subjects about its privacy practices to meet the entity's objectives related to privacy. The notice is updated and communicated to data subjects in a timely manner for changes to the entity's privacy practices, including changes in the use of personal information, to meet the entity's objectives related to privacy.
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P2.1 — The entity communicates choices available regarding the collection, use, retention, disclosure, and disposal of personal information to the data subjects and the consequences, if any, of each choice. Explicit consent for the collection, use, retention, disclosure, and disposal of personal information is obtained from data subjects or other authorized persons, if required. Such consent is obtained only for the intended purpose of the information to meet the entity's objectives related to privacy. The entity's basis for determining implicit consent for the collection, use, retention, disclosure, and disposal of personal information is documented.
The entity communicates choices available regarding the collection, use, retention, disclosure, and disposal of personal information to the data subjects and the consequences, if any, of each choice. Explicit consent for the collection, use, retention, disclosure, and disposal of personal information is obtained from data subjects or other authorized persons, if required. Such consent is obtained only for the intended purpose of the information to meet the entity's objectives related to privacy. The entity's basis for determining implicit consent for the collection, use, retention, disclosure, and disposal of personal information is documented.
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P3.2 — For information requiring explicit consent, the entity communicates the need for such consent, as well as the consequences of a failure to provide consent for the request for personal information, and obtains the consent prior to the collection of the information to meet the entity's objectives related to privacy.
For information requiring explicit consent, the entity communicates the need for such consent, as well as the consequences of a failure to provide consent for the request for personal information, and obtains the consent prior to the collection of the information to meet the entity's objectives related to privacy.
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P5.1 — The entity grants identified and authenticated data subjects the ability to access their stored personal information for review and, upon request, provides physical or electronic copies of that information to data subjects to meet the entity's objectives related to privacy. If access is denied, data subjects are informed of the denial and reason for such denial, as required, to meet the entity's objectives related to privacy.
The entity grants identified and authenticated data subjects the ability to access their stored personal information for review and, upon request, provides physical or electronic copies of that information to data subjects to meet the entity's objectives related to privacy. If access is denied, data subjects are informed of the denial and reason for such denial, as required, to meet the entity's objectives related to privacy.
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P5.2 — The entity corrects, amends, or appends personal information based on information provided by data subjects and communicates such information to third parties, as committed or required, to meet the entity's objectives related to privacy. If a request for correction is denied, data subjects are informed of the denial and reason for such denial to meet the entity's objectives related to privacy.
The entity corrects, amends, or appends personal information based on information provided by data subjects and communicates such information to third parties, as committed or required, to meet the entity's objectives related to privacy. If a request for correction is denied, data subjects are informed of the denial and reason for such denial to meet the entity's objectives related to privacy.
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P6.1 — The entity discloses personal information to third parties with the explicit consent of data subjects, and such consent is obtained prior to disclosure to meet the entity's objectives related to privacy.
The entity discloses personal information to third parties with the explicit consent of data subjects, and such consent is obtained prior to disclosure to meet the entity's objectives related to privacy.
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P6.2 — The entity creates and retains a complete, accurate, and timely record of authorized disclosures of personal information to meet the entity's objectives related to privacy.
The entity creates and retains a complete, accurate, and timely record of authorized disclosures of personal information to meet the entity's objectives related to privacy.
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P6.7 — The entity provides data subjects with an accounting of the personal information held and disclosure of the data subjects' personal information, upon the data subjects' request, to meet the entity's objectives related to privacy.
The entity provides data subjects with an accounting of the personal information held and disclosure of the data subjects' personal information, upon the data subjects' request, to meet the entity's objectives related to privacy.
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P7.1 — The entity collects and maintains accurate, up-to-date, complete, and relevant personal information to meet the entity's objectives related to privacy.
The entity collects and maintains accurate, up-to-date, complete, and relevant personal information to meet the entity's objectives related to privacy.
control · Context
P8.1 — The entity implements a process for receiving, addressing, resolving, and communicating the resolution of inquiries, complaints, and disputes from data subjects and others and periodically monitors compliance to meet the entity's objectives related to privacy. Corrections and other necessary actions related to identified deficiencies are made or taken in a timely manner.
The entity implements a process for receiving, addressing, resolving, and communicating the resolution of inquiries, complaints, and disputes from data subjects and others and periodically monitors compliance to meet the entity's objectives related to privacy. Corrections and other necessary actions related to identified deficiencies are made or taken in a timely manner.
control · Context
ITGC-CM — Program change management — changes to applications, databases, and infrastructure are requested, authorized, tested, approved, and migrated to production by appropriate personnel with segregation between development and production.
Program change management — changes to applications, databases, and infrastructure are requested, authorized, tested, approved, and migrated to production by appropriate personnel with segregation between development and production.
risk · Direct
AI accountability gaps and organizational liability
Ambiguous responsibility across developers, deployers, and operators means no party is clearly accountable when AI causes harm; organizations face reputational damage, regulatory sanctions, and civil liability from AI failures at scale, and operational disruption when AI is unavailable.
risk · Direct
Adversarial attacks, data poisoning and prompt injection
Data-poisoning corrupts training data and embeds backdoors; adversarial evasion, prompt injection, and jailbreaks fool deployed models at inference; model extraction steals proprietary weights/logic — enabling harmful or policy-violating outputs.
risk · Direct
Unauthorized or unsafe autonomous agent actions and tool calls
AI agents with excessive permissions or weak action controls execute tool calls, transactions, or code outside their authorized task scope — through prompt injection, misinterpretation, or emergent behaviour — causing data loss, financial loss, or irreversible changes in connected systems.
risk · Direct
Harmful AI bias and discrimination against protected groups
Models encode/amplify historical bias, producing allocative harm (biased hiring/lending/housing/benefits), representational harm (stereotyping), and evaluation bias masking disparate subgroup performance — systematically disadvantaging protected groups.
risk · Direct
Misuse of AI systems for offensive cyber operations or catastrophic harm
Users obtain meaningful uplift from an AI system for offensive cyber operations (malware development, vulnerability exploitation, autonomous intrusion) or for biological, chemical, nuclear, or radiological harm, exposing the deploying organization to severe legal, regulatory, and societal consequences.
risk · Direct
Emergent behaviour and unsafe AI system integration
Large-scale or multi-model pipelines exhibit emergent capabilities/failures not present in any component and not predictable from component testing; integration with legacy systems introduces interface mismatches and configuration errors.
risk · Direct
AI endpoint abuse, scraping and model extraction
Adversaries scrape inference endpoints at scale to extract model behaviour or proprietary data, exhaust compute budgets through unbounded consumption, or harvest system prompts and technical details disclosed in outputs or documentation, degrading service and eroding competitive and security posture.
risk · Direct
Environmental footprint of AI training and infrastructure
Training and large-scale inference consume disproportionate energy and generate greenhouse-gas emissions; rapid AI-hardware obsolescence produces e-waste and pressures critical-mineral supply chains, with environmental and geopolitical risk.
risk · Direct
GPAI transparency, systemic-risk and synthetic-content obligations
GPAI providers failing transparency/copyright/training-data obligations; systemic-risk models (>10^25 FLOPs) lacking red-teaming, incident reporting, and cybersecurity; unlabeled deepfake/synthetic content; and concentration of GPAI capability creating ecosystem single points of failure.
risk · Direct
Rights harm from biometric-identification AI
Because remote biometric identification, categorization, or emotion-recognition systems (EU AI Act Annex III(1)) are deployed without conformity assessment, data governance, human oversight, accuracy/robustness controls, or a fundamental-rights impact assessment, they can misidentify, misclassify, or surveil individuals, resulting in wrongful treatment, discrimination, and rights violations.
risk · Direct
Public-safety harm from AI in critical infrastructure
Because AI acting as a safety component in critical digital infrastructure, road traffic, or utilities (Annex III(2)) operates without the required risk management, robustness, and human oversight, it can fail or behave unsafely, resulting in service disruption and threats to public safety and continuity.
risk · Direct
Unfair exclusion by AI in education and training
Because AI determining access, admission, assessment, or proctoring in education and vocational training (Annex III(3)) operates without bias controls, transparency, human oversight, or fundamental-rights safeguards, learners can be scored or excluded unfairly, resulting in denial of educational opportunity and discrimination.
risk · Direct
Discriminatory outcomes from AI in employment
Because AI used for recruitment, selection, promotion, task allocation, or worker monitoring (Annex III(4)) operates without bias mitigation, worker transparency, human oversight, or a data-protection impact assessment, it can decide about workers on biased or opaque grounds, resulting in discriminatory or unfair employment outcomes.
risk · Direct
Unlawful denial of essential services by AI
Because AI evaluating eligibility for public benefits, creditworthiness, or insurance risk and pricing (Annex III(5)) operates without fairness, explainability, human oversight, or the required conformity controls, it can wrongly deny or misprice essential services, resulting in unlawful exclusion and consumer harm.
risk · Direct
Harm to due process and democratic integrity from AI
Because AI assisting judicial decisions or intended to influence elections or voter behaviour (Annex III(8)) operates without human oversight, transparency, and integrity safeguards, it can distort legal outcomes or manipulate electorates, resulting in threats to due process and democratic integrity.
risk · Direct
Rights violations from AI in law enforcement
Because AI for individual risk assessment, evidence evaluation, profiling, or predictive policing (Annex III(6)) operates without strict accuracy, human oversight, logging, and fundamental-rights safeguards, it can drive wrongful enforcement action, resulting in unjust detention, profiling harm, and rights violations.
risk · Direct
Wrongful denial by AI in migration and border control
Because AI for migration, asylum, or visa risk assessment and border-control screening (Annex III(7)) operates without the required accuracy, human oversight, and fundamental-rights protections, it can misjudge individuals, resulting in wrongful denial of entry or status and discrimination.
risk · Direct
Inaccurate, unreliable or hallucinated AI outputs
AI outputs contain factual errors, hallucinations, or confidently wrong predictions; inappropriate proxy metrics, overfitting/underfitting, or insufficient pre-deployment testing undermine trust in decisions made on their basis.
risk · Direct
Inappropriate human/AI task allocation and end-of-life risk
Tasks needing contextual judgment, ethics, or accountability are improperly delegated to AI; and decommissioning raises data-persistence in weights, loss of institutional knowledge, and service-continuity gaps.
risk · Direct
Insecure AI-generated code and hallucinated or typosquatted dependencies
Code-generating AI produces insecure defaults (injection-prone queries, weak authentication and session handling, unsafe logging) or specifies non-existent, hallucinated, or typosquatted packages that attackers pre-register, introducing vulnerabilities and malicious dependencies into production software.
risk · Direct
Insufficient human oversight and automation complacency
Because consequential AI decisions run under full automation with reviewers who lack the authority, information, or AI literacy to intervene, meaningful human control is absent and automation complacency erodes vigilance, so erroneous or harmful automated decisions reach individuals unchecked.
risk · Direct
Lack of AI explainability, documentation and disclosure
Because AI models are opaque, model cards and datasheets are missing, and AI involvement in consequential interactions is not disclosed, affected individuals cannot obtain recourse and operators cannot see failure modes, resulting in unaccountable decisions, uninformed consent, and misplaced confidence in spurious, non-causal model reasoning.
risk · Direct
Model/data drift and inadequate post-deployment monitoring
Distribution shift between training and deployment data silently degrades accuracy, and without ongoing monitoring, model decay and emerging failure modes go undetected with no trigger to retrain or decommission. Uncontrolled updates alter behaviour and invalidate prior assessments.
risk · Direct
Insufficient AI resilience and fallback mechanisms
AI systems lacking fallback, redundancy, or graceful degradation fail catastrophically under adversarial conditions, infrastructure outages, or out-of-distribution inputs, disrupting dependent business processes.
risk · Direct
Poor-quality, unrepresentative or mislabeled training data
Training data that is too small, unrepresentative, error-contaminated, or mislabeled produces systematic failure modes; data-lifecycle risks (unlawful collection, insecure storage, failure to purge) further corrupt model quality or violate privacy.
risk · Direct
AI power concentration and erosion of societal trust
Disproportionate access to data, compute, and AI talent creates winner-take-all dynamics foreclosing competition; proliferation of AI-generated synthetic media and automated influence operations degrades the shared epistemic environment and democratic institutions.
risk · Direct
AI privacy leakage and re-identification
Model inversion and membership-inference attacks reconstruct training data or reveal individuals in the training set; AI inference re-identifies anonymized data and infers sensitive attributes; training on data without consent/legal basis creates regulatory liability.
risk · Direct
Deployment of prohibited AI practices (EU AI Act Art.5)
Use of prohibited AI: subliminal/manipulative techniques, social scoring, untargeted facial-image scraping, real-time/post remote biometric identification for law enforcement, sensitive-attribute biometric categorization, and emotion recognition in work/education settings.
risk · Direct
AI safety failures causing physical or psychological harm
AI errors in safety-critical systems (autonomous vehicles, medical devices, industrial controls) cause injury or death; safety-constraint violations by agentic AI, cascading failures across coupled systems, and AI-generated misinformation/deepfakes cause harm.
risk · Direct
Credential and secret leakage through AI inputs, outputs, logs and generated code
API keys, tokens, private keys, and connection strings pasted into prompts, returned in outputs, hardcoded in generated code, or captured in conversation logs are exposed to unauthorized parties or persisted outside secret management, enabling account takeover and lateral movement.
risk · Direct
AI supply-chain compromise and provider concentration
Because the organization relies on third-party pretrained models, datasets, and libraries that may carry backdoors, malicious code, or bias, and concentrates on a few external AI API providers, AI-dependent workflows are exposed to both supply-chain compromise and provider outage or insolvency, resulting in compromised model behaviour or sudden loss of AI capability.
risk · Direct
Privacy harms: distortion, stigmatization, unwarranted restriction
Processing inaccurate/out-of-context data (distortion), attaching negative social labels (stigmatization), or denying services based on personal data without justification (unwarranted restriction / algorithmic gatekeeping) — causing discrimination and economic loss.
risk · Direct
Power imbalance and loss of self-determination over personal data
Structural informational asymmetry (take-it-or-leave-it consent, opaque algorithmic decisions) and inability to correct, delete, or restrict processing deprive individuals of meaningful control over their own data and narrative.
risk · Direct
Innovation shortfall and emerging-technology adoption risk
Because the organization under-invests in or poorly governs its innovation pipeline while adopting unproven emerging technologies (AI/ML, blockchain, cloud) without adequate diligence, it faces both loss of differentiation and obsolescence and implementation failure, vendor lock-in, or ethical exposure, resulting in eroded competitiveness and failed technology bets.
standard · Context
AIUC-1 (Jul 2026)
AIUC-1 — AI agent security, safety and reliability standard (requirement level)
standard · Context
CCPA/CPRA
CCPA/CPRA — California Consumer Privacy
standard · Context
COBIT 2019
COBIT 2019 Governance & Management Objectives
standard · Context
COSO ERM 2017
COSO ERM – Integrating with Strategy and Performance (2017)
standard · Context
COSO IC 2013
COSO Internal Control – Integrated Framework (2013)
standard · Context
EU DORA
EU DORA — Digital Operational Resilience Act
standard · Context
EU AI Act
EU AI Act — principal obligation areas (Chapters II, III, V and IX)
standard · Context
EU GDPR
EU GDPR — General Data Protection Regulation
standard · Context
HIPAA
HIPAA — Security, Privacy & Breach Notification
standard · Context
IIA 2024 Standards
IIA 2024 Global Internal Audit Standards
standard · Context
The Role of the Internal Audit Function in Enterprise Risk Management
The Role of the Internal Audit Function in Enterprise Risk Management
standard · Context
Three Lines Model: Assurance and Advice in Support of Effective Governance
Three Lines Model: Assurance and Advice in Support of Effective Governance
standard · Context
ISO/IEC 27001:2022
ISO/IEC 27001:2022 Annex A
standard · Context
ISO 31000:2018
ISO 31000:2018 Risk Management (principles/framework/process)
standard · Context
ISO/IEC 42001:2023 (AI)
ISO/IEC 42001:2023 Annex A (AI Management System)
standard · Context
EU NIS2
EU NIS2 Directive
standard · Context
NIST SP 800-53 Rev5
NIST SP 800-53 Rev 5 — Security and Privacy Controls
standard · Context
NIST Agent Identity (draft, Feb 2026)
NIST NCCoE: Software and AI Agent Identity and Authorization
standard · Context
NIST TEVV-Athlon (draft, Aug 2026)
NIST AI 200-2: TEVV-Athlon Framework for Evaluating AI Systems
standard · Context
NIST CSF 2.0
NIST Cybersecurity Framework 2.0
standard · Context
NYDFS Part 500
NYDFS Part 500 — NY Cybersecurity Regulation
standard · Context
PCI DSS v4.0.1
PCI DSS v4.0.1
standard · Context
SOC 1
SOC 1 (SSAE 18 / ISAE 3402) — service-org ICFR control objectives
standard · Context
SOC 2 (TSC)
AICPA SOC 2 Trust Services Criteria (2017, rev. 2022)
standard · Context
SOX / PCAOB (ICFR)
SOX 404 / PCAOB AS 2201 — ICFR control taxonomy
unified · Context
UC-ACCESS-14 — Authorize public content and external information sharing
Actions permitted without identification or authentication are explicitly defined, documented, and limited to designated public functions. Information shared with external parties requires owner authorization consistent with classification and sharing agreements. Only trained, designated individuals may post content to publicly accessible systems, with pre-publication review and periodic checks to ensure no nonpublic information is exposed.
unified · Context
UC-ACCESS-16 — Authorize, test, and approve changes and development
Changes to applications and infrastructure, and new system development, follow a documented lifecycle: authorized request, risk-assessed design, testing in non-production environments, documented approval, and controlled migration to production by personnel independent of development. Emergency changes are ratified retrospectively, and evidence of each gate is retained.
unified · Direct
UC-AI-01 — Maintain and periodically review the AI policy
Establish a management-approved AI policy that sets principles and requirements for developing and using AI systems, and keep it demonstrably consistent with related organizational policies such as security, privacy, and ethics. Review the policy at planned intervals and upon significant regulatory or technology change, recording review outcomes and revisions. Publish the current version to all relevant personnel and retain prior versions as evidence.
unified · Direct
UC-AI-02 — Define AI roles, responsibilities, and competencies
Define and assign roles, responsibilities, and authorities for AI governance and for each AI system life-cycle stage, recording them in a maintained RACI or equivalent register. Document the human resources involved in AI development, deployment, and operation, including the competences each role requires and how competence gaps are addressed. Review assignments and competence records when systems, personnel, or obligations change.
unified · Direct
UC-AI-03 — Document AI system resources and dependencies
Maintain a documented inventory of the resources each AI system depends on, covering data resources, tooling such as frameworks, libraries, and models, and system and computing resources, together with their owners and locations. Record sufficient detail for each resource to support impact assessment, reproducibility, and supplier accountability. Keep the inventory current through the change process and review it periodically.
unified · Direct
UC-AI-04 — Assess impacts and classify AI systems before deployment
Operate a documented AI impact assessment process performed before deployment and repeated on significant change, assessing consequences for individuals, groups of individuals, and society, including fairness, safety, and fundamental-rights effects. Classify each system against applicable regulatory risk categories, including any high-risk designation, and record the classification rationale. Retain assessment reports, approvals, and reassessment triggers as evidence.
unified · Direct
UC-AI-05 — Set responsible AI development objectives and requirements
Define objectives for responsible AI development, such as fairness, safety, security, transparency, and accountability, and embed them in a documented design and development process. Specify and document requirements for each AI system, including intended purpose, performance criteria, and constraints, before build begins. Evidence includes the development process definition, per-system requirement specifications, and design-stage approvals.
unified · Direct
UC-AI-06 — Maintain AI system technical documentation
Produce and maintain technical documentation for each AI system covering design and development decisions, architecture, model and data characteristics, performance, and limitations, including all content mandated by applicable regulation for higher-risk systems. Keep documentation up to date through changes and retain it for the regulatorily mandated period. Documentation must be sufficient for regulators and assessors to evaluate compliance.
unified · Direct
UC-AI-07 — Verify, validate, and control AI deployment and changes
Verify and validate each AI system against its requirements and responsible-AI objectives, documenting test plans, acceptance criteria, and results before release approval. Gate deployment on a documented deployment plan and sign-off confirming requirements are met. Route updates, retraining, and other changes through the same assessment and approval process, including impact reassessment where relevant, and retain verification records and deployment and change approvals.
unified · Direct
UC-AI-08 — Log and monitor AI system behavior in operation
Ensure AI systems automatically record event logs that enable traceability of operation over the system's lifetime, including events relevant to identifying risk situations and substantial modification. Retain logs for at least the mandated regulatory minimum, or longer where required. Monitor deployed systems against defined performance and behavior metrics with alerting and escalation for anomalies and drift, and retain logs and monitoring reviews as evidence.
unified · Direct
UC-AI-09 — Govern AI data quality, provenance, and preparation
Govern training, validation, and test data under documented data-management practices covering acquisition, selection, provenance recording, and preparation activities such as labelling, cleaning, and enrichment. Apply and record quality criteria appropriate to the intended purpose, including relevance, representativeness, and, to the best extent possible, completeness and freedom from errors, and examine datasets for biases with mitigation of those likely to affect health, safety, or fundamental rights. Retain data documentation and provenance records for each AI system.
unified · Direct
UC-AI-10 — Meet transparency obligations for AI systems
Provide users, deployers, and other interested parties with documentation and instructions for each AI system describing its intended purpose, capabilities, limitations, performance characteristics, and required human-oversight measures. Disclose when people are interacting with an AI system, and mark AI-generated or manipulated content, including deepfakes, in a machine-readable and clearly perceptible way where required. Keep transparency materials current with system changes and retain distribution evidence.
unified · Direct
UC-AI-11 — Operate AI concern, incident, and external reporting channels
Operate channels through which personnel and other stakeholders can report concerns about the organization's role in developing or using AI systems, with documented triage and follow-up. Define and execute procedures for communicating AI incidents to affected users and interested parties, and for making required reports to regulators and other external bodies within mandated timeframes. Retain concern reports, incident communications, and resolution records as evidence.
unified · Direct
UC-AI-12 — Enforce responsible and lawful use of AI systems
Define objectives and documented processes for responsible use of AI systems, and ensure each system is used only in accordance with its intended use and accompanying documentation. Screen proposed and existing uses against legal prohibitions on unacceptable practices — such as manipulative techniques, social scoring, and untargeted facial-image scraping — and block or discontinue prohibited uses. Retain use approvals and screening records.
unified · Direct
UC-AI-13 — Assign AI value-chain roles and discharge obligations
For each AI system, determine and document the organization's role in the value chain, such as provider or deployer, and allocate life-cycle responsibilities among the organization, partners, suppliers, and customers. Maintain a register of the obligations attached to each role, including provider duties for high-risk systems (quality management, conformity assessment, CE marking, registration) and deployer duties (instruction-compliant operation, oversight assignment, log retention), tracking each obligation to an owner and evidence. Review the register when systems or regulations change.
unified · Direct
UC-AI-14 — Manage responsible AI with suppliers and customers
Govern supplier relationships that provide AI systems, components, or data through a documented process that verifies supplier alignment with the organization's responsible-AI requirements. Ensure the organization's responsible development and use of AI considers customer needs and expectations, and provide customers with the information they need to use AI systems responsibly. Retain supplier evaluations and customer communications as evidence.
unified · Direct
UC-AI-15 — Fulfill general-purpose AI model provider obligations
Where the organization provides general-purpose AI models, maintain model technical documentation and information for downstream providers, implement a policy to comply with applicable copyright law including reservation-of-rights opt-outs, and publish a sufficiently detailed summary of training content. For models designated as posing systemic risk, additionally perform state-of-the-art model evaluations including adversarial testing, assess and mitigate systemic risks, track and report serious incidents to the competent authority, and ensure adequate cybersecurity protection for the model and its infrastructure.
unified · Direct
UC-AI-16 — Ensure human oversight of AI decisions
Design and operate high-risk AI systems so that natural persons can effectively oversee them during use, through human-machine interface tools and oversight measures built into the system or defined for the deployer. Assign oversight to persons who understand the system's capacities and limitations, can monitor operation, remain aware of automation bias, correctly interpret output, and can decide not to use, override, or halt the system. Evidence includes oversight procedures, oversight assignments, and intervention records.
unified · Direct
UC-AI-17 — Define customer data-use and output-rights policies for AI services
Publish and maintain customer-facing policies for AI services that state how customer inputs are used for model training and inference, how long inputs and outputs are retained, who owns and may use generated outputs, and how customers can opt in or out, request deletion, or exercise other data rights. Communicate the policies before onboarding, version them, and retain customer acknowledgements and periodic review records as evidence.
unified · Direct
UC-AI-18 — Defend AI interfaces against adversarial input, injection, and endpoint abuse
Protect the inference and agent interfaces of AI systems with layered input defenses: screen prompts, uploaded content, retrieved data, and tool results for prompt-injection and jailbreak patterns before they reach the model or trigger actions; detect and alert on adversarial-input campaigns; and rate-limit, authenticate, and monitor endpoints to prevent scraping, model extraction, and resource-exhaustion abuse. Tune detections from evaluation findings and retain filter configurations and detection logs as evidence.
unified · Direct
UC-AI-19 — Constrain agent actions and tool use to authorized scope
Bound what autonomous agents may do: allow-list the tools, connectors, and actions each agent may invoke; scope its permissions to the task, user, and context; require human approval for irreversible, high-value, or out-of-policy actions; execute agent-generated code only in isolated sandboxes; and scan agent configuration artifacts such as hooks, skills, and rules for injected instructions. Log every tool call with its authorization decision and review denied and escalated calls.
unified · Direct
UC-AI-20 — Prevent harmful, out-of-scope, hallucinated, and over-exposed AI outputs
Filter and shape every AI output before release: block or transform content that matches the system's harmful-output taxonomy, keep responses within the declared scope and capabilities, detect agent-specific high-risk outputs and route them to defined responses by severity, ground factual claims in cited sources and verify them to limit hallucination, withhold system prompts, internal data, and other over-exposed information, and sanitize outputs consumed by downstream systems so they cannot carry executable or injected payloads. Measure filter effectiveness and retain configurations, block logs, and review samples as evidence.
unified · Direct
UC-AI-21 — Commission independent third-party AI evaluations on a quarterly cadence
Engage an independent evaluator at least quarterly to test each in-scope AI system against its defined risk categories: adversarial robustness and jailbreak resistance, harmful and out-of-scope outputs, agent-specific high-risk outputs, hallucination rates, and unsafe or unauthorized tool calls. Fix the test scope and pass thresholds in advance, track every finding to remediation and retest, and retain evaluator reports, methodologies, and remediation evidence.
unified · Direct
UC-AI-22 — Prevent leakage of credentials and secrets through AI systems
Detect credentials, tokens, private keys, and connection strings in prompts, pasted content, retrieved data, and generated code using pattern- and entropy-based scanning; warn users and block or redact detected secrets before model processing, logging, and storage; guide code-generating systems to reference secret managers and environment variables rather than hardcoding credentials; and store any user-supplied credentials only in a dedicated secret manager encrypted at rest. Retain detection rules, redaction logs, and storage configurations as evidence.
unified · Direct
UC-AI-23 — Prevent misuse of AI systems for cyber offense and catastrophic harm
Recognize and refuse requests that seek meaningful uplift for offensive cyber operations or for biological, chemical, nuclear, or radiological harm, calibrated to the capability level of the deployed models; monitor for misuse patterns, escalate and report confirmed attempts, and keep refusal policies, classifiers, and escalation procedures current as capabilities change. Retain misuse-detection configurations, incident records, and periodic review evidence.
unified · Direct
UC-AI-24 — Operate an AI quality management system
Establish and maintain a documented quality management system for AI products covering quality objectives and responsibilities; documented design, development, testing, and release procedures; change management; data management procedures; issue tracking and corrective action; stakeholder and regulator communication procedures; and record keeping. Review the system periodically for effectiveness and continual improvement, and retain the quality manual, procedures, review records, and corrective-action logs as evidence.
unified · Direct
UC-AI-25 — Guide code-generating systems toward secure patterns and safe dependencies
Configure code-generating AI systems with secure-by-default guidance: steer generated code toward parameterized queries and safe frameworks for common vulnerability classes, established authentication and authorization libraries, secure session and cookie settings, input validation and safe error handling, and logging that excludes secrets; require pinned, verified dependency specifications so hallucinated or typosquatted packages are not introduced; and test the guidance against a vulnerability benchmark on change. Retain system-prompt and policy configurations and benchmark results as evidence.
unified · Context
UC-AUDIT-03 — Ensure board oversight and support of internal audit
The board oversees the internal audit function through regular interaction with the chief audit executive, including executive sessions without management present, and approves the CAE's appointment, remuneration, evaluation, and removal. The board reviews and approves the internal audit plan and budget and ensures the function has sufficient resources to fulfill its mandate. Board meeting minutes and approvals evidence oversight.
unified · Context
UC-AUDIT-06 — Ensure auditor competency and continuing development
The internal audit function collectively possesses, and individual auditors apply, the knowledge, skills, and abilities required for their responsibilities, engaging qualified assistance where gaps exist. Auditors maintain and enhance their competency through continuing professional development, which is planned, tracked, and reviewed at least annually. Training records and competency assessments evidence operation.
unified · Context
UC-AUDIT-08 — Protect confidential information obtained in audit work
Internal auditors use information obtained during their work only for legitimate professional purposes and in conformance with applicable laws, regulations, and organizational policies. Information is protected against unauthorized access, use, or disclosure during and after engagements, and confidentiality obligations extend to parties assisting the internal audit function. Confidentiality acknowledgments and access controls over audit files evidence operation.
unified · Context
UC-AUDIT-23 — Coordinate independent assurance reviews across providers
The organization plans and obtains independent reviews of its approach to managing and implementing information security - including people, processes, and technologies - at planned intervals, after significant changes, and where required by applicable law or regulation. Before relying on another provider's work, each reliance decision assesses and records the provider's independence and objectivity, competence and methodology rigor, evidence quality and reperformance capability, and recency against the covered risk's cadence, together with the resulting reliance level and rationale. Assurance activities are coordinated across internal and external providers to ensure coverage, minimize duplication, and support reliance on others' work. Material reliance limitations, assurance gaps, and duplication remain visible to management and the board. Results are reported to management and the board and drive corrective actions.
unified · Context
UC-CONFIG-02 — Authorize, test, and approve changes before production
Manage changes to applications, databases, infrastructure, configurations, and procedures through a documented process in which changes are requested, analyzed for security and risk impact, authorized, tested, approved, and implemented by appropriate personnel. Require migration to production to be performed by individuals independent of development, and retain records evidencing each step. Define rollback plans and an emergency-change path with retrospective review and approval.
unified · Context
UC-DATA-02 — Obtain and honor consent for collection, use, and disclosure
Where consent or authorization is the basis for collecting, using, retaining, disclosing, selling, or sharing personal data, present the available choices and their consequences clearly and capture freely given, specific, informed consent before the data is collected or disclosed. Maintain auditable consent records, honor withdrawal and opt-out requests (including opt-out of sale/sharing and limits on sensitive-data use) as easily as consent was given, and use compliant authorization forms where required. Document the basis for any implied consent relied upon.
unified · Context
UC-DATA-04 — Restrict processing of special categories of personal data
Inventory all processing of special categories of personal data (e.g., health, biometric, genetic, race or ethnicity, religion, sexual orientation) and prohibit such processing unless a documented legal exception or explicit consent applies. Apply heightened safeguards to this data and record the exception relied upon for each processing activity.
unified · Context
UC-DATA-05 — Provide privacy notices and transparency to data subjects
Publish and maintain privacy notices that describe, in clear and plain language, the categories of personal data collected, purposes, lawful bases, recipients, retention periods, and data-subject rights, and deliver them at or before the point of collection. Update and re-communicate notices in a timely manner when practices change, and publish any legally required registrations such as system-of-records notices. Retain dated notice versions as evidence.
unified · Context
UC-DATA-06 — Provide data subjects access to their personal data
Operate a mechanism for identified and authenticated individuals to obtain confirmation of processing and a copy of their personal data, including the categories collected, sold, shared, or disclosed and the categories of recipients, within statutory deadlines. Where access is denied, inform the individual of the denial, the reason, and any recourse. Log all requests and responses as evidence.
unified · Context
UC-DATA-07 — Keep personal data accurate and honor correction requests
Maintain personal data that is accurate, complete, up to date, and relevant for its intended use, with periodic data-quality checks. Provide a process for individuals to request correction or amendment, execute or formally deny each request within statutory deadlines with stated reasons, and communicate corrections to third parties to whom the data was disclosed.
unified · Context
UC-DATA-08 — Execute deletion and other rights requests within deadlines
Operate a verified rights-request process with designated intake channels, identity verification, and statutory response clocks that executes data-subject rights including erasure/deletion, portability, restriction, objection, and rights related to automated decision-making, and directs service providers to do the same. Do not discriminate or retaliate against individuals for exercising rights, and disclose the material terms of any financial-incentive program with opt-in consent. Track every request end-to-end as evidence.
unified · Context
UC-DATA-12 — De-identify, mask, or pseudonymize personal data
Apply masking, pseudonymization, or de-identification when full identifiers are not required, following policy and the applicable legal standard (e.g., expert determination or safe-harbor methods, limited data sets under agreement). Protect the keys and mappings that could re-identify data, and prohibit re-identification attempts.
unified · Context
UC-DATA-14 — Maintain and provide an accounting of disclosures
Record every authorized disclosure of personal information - recipient, date, data categories, and purpose - completely, accurately, and in a timely manner. Upon a verified request, provide the data subject an accounting of the personal information held and its disclosures within the required timeframe.
unified · Context
UC-DATA-17 — Resolve privacy inquiries, complaints, and disputes
Operate a documented channel for receiving, tracking, addressing, and resolving privacy inquiries, complaints, and disputes from data subjects and other parties. Communicate resolutions to the complainant, make corrections for identified deficiencies in a timely manner, and monitor complaint trends for systemic issues.
unified · Context
UC-DATA-18 — Govern automated matching of PII under formal agreements
Enter formal agreements before participating in automated matching of personal data between organizations or systems, specifying the purpose, data elements, and verification procedures. Provide any required notice, independently verify matched information before taking adverse action against an individual, and retain agreements and verification records as evidence.
unified · Context
UC-RISK-01 — Establish and maintain a tailored risk management framework
Senior leadership establishes, approves, and visibly sponsors an enterprise risk management framework customized to the organization's external and internal context. The framework design defines accountabilities, resources, and processes for managing risk, and the framework is implemented across the organization on a planned schedule. Where regulated technologies such as high-risk AI systems are in scope, the framework is extended with the required lifecycle-specific risk processes. Approved framework documentation and implementation plans are retained as evidence.
unified · Context
UC-RISK-11 — Assess changes that could significantly affect risk and control
The organization identifies and assesses internal and external changes - new business models, leadership, systems, regulations, and operating environment - that could significantly affect its risk profile or system of internal control. Risk assessments and responses are updated dynamically as changes and emerging risks are detected. Change-triggered assessments and resulting updates are documented.
unified · Context
UC-RISK-13 — Monitor and review risk management performance
The organization performs ongoing and separate evaluations of risk management and internal control performance, periodically measuring the framework's effectiveness against its design and intended outcomes. Risk and business performance are reviewed together at defined intervals and results are reported to accountable management. Evaluation schedules, results, and review minutes are retained as evidence.
unified · Context
UC-RISK-15 — Continually improve the risk management program
Lessons from evaluations, monitoring, and operating experience are translated into improvements to the risk management framework and process. Improvement actions are planned, assigned owners, and tracked to completion, and the framework is continually adapted to remain suitable for the organization. Improvement backlogs and completed-action evidence demonstrate operation.
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UC-SDLC-04 — Engineer systems with secure architecture and design
Design and build systems using established secure architecture and engineering principles: least privilege, defense in depth, isolation of execution domains (process and memory separation), fail-safe defaults, and attack-surface minimization, applied from concept through implementation. Require developers to produce and maintain a security architecture description consistent with the enterprise architecture. Engineer solutions to remain accurate, robust, and resilient against errors, faults, and adversarial manipulation.
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UC-SDLC-05 — Enforce secure coding and input validation standards
Establish and enforce secure coding standards for in-house and reused code, covering common weakness classes and secure use of components. Validate all information inputs for syntax, semantics, type, length, and range at trust boundaries, rejecting or safely encoding unsafe input. Verify adherence through code review and static analysis before release.
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UC-SDLC-06 — Maintain configuration control over systems and code
Maintain configuration management over solution components throughout development and operation: identify configuration items, establish and protect baselines for code, dependencies, and build settings, record and verify configuration information, and review deviations. Require developers to track the integrity of changes to configuration items, implement only approved changes, and track and resolve resulting security flaws.
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UC-SDLC-07 — Approve, test, and accept changes before production release
Evaluate, prioritize, and authorize all IT changes, including emergency changes, before implementation, with documented impact and risk assessment. Establish acceptance criteria, perform acceptance testing in an environment representative of production, obtain business and IT approval, and promote releases through a controlled, auditable transition with fallback plans and post-implementation review.
unified · Context
UC-SDLC-10 — Oversee outsourced development and vet developers
Direct, monitor, and review outsourced and third-party development: contractually define secure-development requirements, intellectual property ownership, and audit rights, review deliverables against requirements, and obtain evidence of security testing. Screen developers of critical systems against defined criteria before granting them access to development environments.
unified · Context
UC-SDLC-11 — Apply specialized development to critical components
Identify components critical to security or mission and, where commercial items cannot meet requirements, use customized or specialized development, such as reimplementation, custom variants, or context-specific augmentation, to reduce supply-chain and assurance risk. Document the rationale and assurance evidence for each specialized component.
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UC-SDLC-12 — Manage solution assets and retire unsupported components
Manage application and infrastructure assets through their life cycle: maintain an accurate record of solution assets, ownership, and licensing, and optimize their use and cost. Identify system components approaching end of support and replace or upgrade them, or apply documented compensating controls with explicit risk acceptance, before support lapses.
unified · Context
UC-TPRM-01 — Operate a third-party security risk management program
Establish and operate a management-approved third-party and supply-chain risk management program with a written strategy, policies, and procedures, reviewed at defined intervals and after significant changes to the supply chain or threat landscape. Define and communicate roles and responsibilities for supplier, customer, and partner relationships, and integrate third-party and supply-chain risk into enterprise and cybersecurity risk management. Maintain a register of third-party relationships and contractual arrangements prioritized by criticality, and assess criticality, substitutability, and concentration risk before contracting. Apply risk-based due diligence, embed security requirements, audit and access rights, termination rights, and sub-outsourcing conditions in agreements, and protect organizational information processed, stored, or transmitted on external systems. Define, agree, and periodically review service agreements and supplier performance, reassess third parties on a defined cycle, operate controls to identify and address weaknesses across the relationship life cycle, and maintain documented, tested exit strategies for providers supporting critical or important functions.
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UC-TPRM-02 — Perform risk-based due diligence before engaging vendors
Before entering a formal relationship, perform security due diligence on prospective vendors and business partners proportionate to their criticality, evaluating security posture, financial and operational risk, and supply-chain exposure, and document the acceptance decision. Use acquisition strategies, sourcing methods, and selection criteria designed to reduce supply-chain risk before contract award.
unified · Context
UC-TPRM-07 — Verify component authenticity, provenance, and integrity
Document and maintain the provenance of critical systems, components, and data through the supply chain, for example with bills of materials and chain-of-custody records. Apply anti-tamper and anti-counterfeit measures: tamper-resistant and tamper-evident packaging and design, inspection of systems and components at receipt and on indication of tampering, and verification of component authenticity with training and reporting of suspected counterfeits.
unified · Context
UC-TPRM-08 — Govern security of external and cloud service use
Define and enforce processes for acquiring, using, managing, and exiting external system services and cloud services in line with the organization's information security requirements. Require external providers to comply with those requirements, define oversight roles and responsibilities on both sides, agree service and exit terms, and monitor provider compliance on an ongoing basis.
unified · Context
UC-VULN-04 — Test software security during development and acceptance
Require developers and project teams to perform security testing throughout development and at acceptance, including a documented test plan, static and dynamic analysis appropriate to the technology, and retained evidence of test execution and results. Define security acceptance criteria for new systems and major upgrades, and remediate weaknesses found before release into production.
unified · Context
UC-VULN-09 — Employ non-persistence and information-resilience techniques
Reduce the attack surface available to persistent adversaries by provisioning selected components and services non-persistently and refreshing them from known-good, trusted sources at defined intervals or on demand. Refresh designated information at defined frequencies to purge stale or potentially corrupted data, source critical information from diverse suppliers or paths, and fragment designated high-value information across separate systems so no single compromise exposes or destroys it.
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Cybersecurity Assurance Review
Cybersecurity Assurance Review — a CAE-owned assurance engagement that runs on the EXISTING Audit item opened from the audit plan (audit_type=it_audit, status PLANNED, lead_auditor and scope already set): the workflow instance attaches to that item and enriches it end to end, never creating a duplicate engagement record. It covers the three IIA Cybersecurity Topical Requirement domains (governance, risk management, and control activities) over the cyber estate bounded in the engagement memo (in scope: named legal entities, networks, cloud tenants, and OT/ICS where included; out of scope: areas whose assurance is documented as delivered by other engagements), testing against the NIST 800-53 Rev 5 catalog with CSF 2.0 / ISO 27001 as the aggregation frame. It originates from the audit plan (no upstream workflow) and produces the findings register (one four-Cs Issue per finding), the cyber posture summary carrying the per-domain and overall Standard 14.5 conclusions, and the approved engagement package — which it hands to the downstream Audit Report Drafting workflow.
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Internal Audit Ethics, Objectivity & Competency Program
Runs on an Audit item created per cycle as the anchor — audit_type=internal, scope set to the IA professional-practice program for the period, period_start/period_end = the cycle window (a program cycle, not an engagement, so this is a documented reuse of the Audit type; it is the "cycle item" every stream links its evidence to and closes at the end). A decision-aware annual cycle that attests the ethics and professional-courage expectations and documents deviations, screens per-engagement conflicts and manages objectivity impairments, collects confidentiality acknowledgments and restricts audit-file access, and assesses competency against role requirements with approved, tracked continuing-professional-development plans for each auditor. It consumes no upstream workflow: prior-cycle carryover (unresolved-deviation and monitored-impairment Issue items still open against the prior cycle's Audit item, plus in-progress development plans) is its own input, and the population is confirmed against the HR roster, engagement staffing, and the audit-file access list before measurement begins. Named deliverables: the professional-practice requirements memo, the ethics attestation register, the conflict-of-interest declarations and impairment register, the confidentiality acknowledgment register and before/after audit-file access review, the competency assessments with coverage matrix and CPD plans, and the signed CAE conformance report to the audit committee — assembled into an indexed cycle evidence file on the anchor Audit item. Downstream is self-feeding: the carry-forward list produced at close hands off to the next run of this same workflow; there is no distinct downstream workflow. In scope: every auditor and assisting party (employees plus co-source, outsourced, and guest auditors) who performed internal audit work or holds audit-file access during the period, across all four expectation streams (ethics, objectivity, confidentiality, competency); out of scope: the audit engagements' own subject-matter conclusions and any HR, legal, or ethics-office investigation a disclosed concern is referred into.
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Fraud & Forensic Investigation Engagement
Fraud & Forensic Investigation Engagement as a decision-aware workflow. It runs on a dedicated Audit item (audit_type: investigation) created for this allegation at intake — the confidential case record — with the workflow instance attached to that item and its visibility restricted to the named investigation team. In scope: one specific fraud allegation, worked predication-gated and confidentially from intake through evidence preservation, forensic procedures, interviews, loss quantification, and audit-committee reporting; the named deliverables are the chain-of-custody register, the findings memorandum with its loss-quantification schedule, and the privilege-marked audit-committee fraud report. Out of scope: the enterprise fraud risk profile (owned by Fraud Risk Assessment & Anti-Override Control Review, which receives scheme intelligence from this case rather than being rerun here) and any unrelated conduct discovered in passing (which gets its own intake record). It consumes the hotline intake package from Control Responsibility Communications & Ethics Hotline when so routed, and hands each control breakdown off as an Issue item — control-gap findings to Finding Remediation & Action-Plan Monitoring and ICFR-affecting deficiencies to SOX Deficiency Remediation — rather than duplicating that work.
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SOC 2 Trust Services Readiness
Runs on the existing Audit engagement with its system description, service commitments, review period, control and risk registers, and available evidence; assesses CC1–CC9 design readiness and consumes reviewed companion assessments for selected optional Trust Services categories. Delivers the criterion-to-control mapping, criterion-level evidence and design conclusions, owned gap register, and approved SOC 2 readiness disposition to management for remediation and examination planning; Type II testing, management-owned PBC preparation and management assertion remain separate workflows.
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ITGC Change & Provisioning Testing
Runs ON a control item (a change-management or access-provisioning ITGC). Validates the change and provisioning populations for the period, draws reproducible samples, tests each sampled ticket against the control attributes (approval before migration, segregation of developer and migrator, approval before access, access matches the requested profile), records design and operating conclusions on the control, and raises every exception as an issue.
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Quality Assurance & Improvement Program Cycle
Operate the Quality Assurance & Improvement Program (QAIP) cycle: ongoing-monitoring evidence, periodic self-assessment, external quality assessment (EQA) support, improvement planning, and board reporting. This cycle runs on an Audit item created per cycle (audit_type = internal — the schema has no quality_assessment option; scope = "QAIP cycle FYxx"; period_start/period_end span the period under assessment); the workflow instance attaches to that Audit item and every cycle output — the per-standard conformance ratings matrix, the below-GC finding Issue items, the improvement and action plan, and the QAIP results report — links back to it. It consumes the period's existing engagement Audit items and their completed engagement-workflow runs as the population and test evidence, plus the standing QAIP framework, charter, and methodology-manual Policy items. In scope: assessing the internal audit function's conformance with the Global Internal Audit Standards for the period. Out of scope: engagement-level rework — this cycle assesses quality, it does not redo fieldwork, which belongs to the engagement workflows. There is no upstream feeder; this workflow starts the quality chain and hands its approved results — overall conclusion, per-domain ratings, and conformance-statement wording — to Quarterly Board & Audit-Committee GRC Reporting.
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Third-Party Vendor Assurance Engagement
Runs on the existing Audit item for this engagement (audit_type=vendor_review) — the workflow enriches that already-planned engagement record, it never creates a duplicate — consuming the confirmed scope, criteria, and calendar handed off from Audit Engagement Planning. An IA-led third-party vendor assurance engagement that concludes on the design and operating effectiveness of the organization’s TPRM program — governance, risk tiering, vendor control-environment reliance, monitoring, exclusions, and reporting. Vendors under test are the existing Vendor items, each finding is an Issue item, and the named deliverable is a reperformable engagement workpaper package. In scope: assuring the program (IA evaluates management’s third-party risk management; it does not operate it). Out of scope: operating the vendor lifecycle (onboarding, tier refresh, remediation), which belongs to the second-line Third-Party Vendor Risk Lifecycle workflow; deep single-report SOC work, which can be delegated to the reusable Vendor SOC 1/SOC 2 Report Review & CUEC Mapping workflow; and ICT arrangements caught by regulatory regimes, which route to Third-Party ICT Vendor Regulatory Assurance. Findings and the engagement conclusion exit through Audit Report Drafting, and action plans route to Finding Remediation & Action-Plan Monitoring.
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Internal Audit Charter, Independence & Board Governance Cycle
Runs on one Audit item created per governance cycle (audit_type: internal; scope set to the internal-audit charter/independence/board-governance cycle for the period) — the workflow instance attaches to that cycle item and writes to it throughout. The internal audit function and its board-approved charter — a Policy item (policy_type: charter) with its own version lineage — already exist and are reviewed, reaffirmed, or amended here, never recreated. The cycle as a decision-aware procedure: the CAE delivers functional reporting to the audit committee, affirms organizational independence in writing and treats any impairment, reviews and reapproves the board mandate and charter with its unrestricted-access provisions, runs the executive session and committee action on the CAE and the plan and budget, executes the stakeholder communication plan, and retains the governance evidence. Consumes upstream: closed assurance-engagement records (Audit items with their linked Issue findings) produced by the individual engagement workflows, the recommendation-tracking register (Issue items), and the prior cycle's carry-forward (open Issue items plus the prior run's carry-forward list). Named deliverables: the CAE functional reporting pack, the written organizational-independence affirmation, the reaffirmed or reapproved audit charter, the audit-committee minutes and resolution records, the stakeholder communication log, and the control-linked governance evidence set. In scope: the board-governance cycle for the internal audit function itself — charter, independence, committee reporting, and stakeholder communication; out of scope: the individual assurance engagements whose results feed the committee report, which run under their own workflows. Terminal by design: no downstream workflow is chained from this cycle; open threads carry forward to seed the next run of this same cycle.
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Continuous Controls Monitoring (ISCM) Cycle
Run the continuous controls monitoring (ISCM) cycle: pull the current-period control metrics and score them against thresholds, triage degraded and failed controls, update the POA&M, report control health to governance, recalibrate the monitoring strategy, then classify the disposition and prepare, hand off, and archive the cycle package. Each interval runs as one workflow instance attached to the existing Process item that represents the ISCM / continuous-controls-monitoring program (process_type = security_process) — enrich that program record every cycle, never create a duplicate. The monitored control set is the existing Control items (Control.frequency doubles as the monitoring cadence, Control.control_owner as the accountable owner) and the POA&M is the existing Issue register (issue_type = deficiency, source = self_assessment); metric definitions and pass/degraded/fail threshold bands have no native field, so they live in the ISCM strategy document carried on the anchor Process item. The cycle produces the control-health scorecard, the reconciled POA&M, the control-health / security-status report, and the recalibrated ISCM strategy. In scope: the recurring NIST 800-137 monitoring loop — metric collection, threshold comparison, triage, POA&M maintenance, security-status reporting, and monitoring-strategy tuning for the controls under continuous monitoring. Out of scope: formal security control assessment and driving gap remediation to closure, which is owned by the downstream Security Control Assessment & POA&M Remediation workflow that consumes this cycle's handoff package. No upstream workflow feeds this one; it is triggered by the arrival of the monitoring interval.
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Technical Security Testing & Pentest Engagement
Runs ON an existing Audit item (audit_type: it_audit) that represents the authorized penetration-test engagement — the workflow instance attaches to that record and enriches it (scope, ratings, dates, and the assurance conclusion write back to its fields); it never creates a duplicate engagement record. In scope: authorized technical testing (reconnaissance, discovery, exploitation validation, severity rating, reporting, and retest) of the defined system boundary against its control baseline and assessment objective, with each confirmed finding recorded as an Issue linked to the anchor Audit and its affected Controls. Out of scope: any testing beyond the agreed rules of engagement, and the downstream remediation program itself — confirmed control gaps and open POA&M findings are handed to the Security Control Assessment & POA&M Remediation workflow, and the assurance conclusion to the Cybersecurity Assurance Review workflow. No upstream workflow feeds this engagement; its inputs are the anchor Audit, the in-scope system boundary (Process items), the control baseline (Control items, framework nist-800-53), the assessment objective and signed authorization, and any open Issue items (source: penetration_test / vulnerability_scan) from prior engagements.
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ISMS Internal Audit & Management Review
Runs one ISO 27001 clause 9.2 internal audit and clause 9.3 management review cycle — including clause 10.1 corrective actions — against the existing Audit item for this cycle (audit_type=internal), whose scope, lead_auditor, and period dates already carry the ISMS audit-programme entry: the workflow enriches that Audit item and its findings, never creates a duplicate audit. Upstream it consumes the Annex A control population (Control items, framework iso-27001) and the applicability decisions in the Statement of Applicability, the risk register (Risk items) and treatment plan, the prior-cycle Audit and open Issue records, and the org's ISMS policies and procedures (Policy items) as audit criteria. Named deliverables: the internal audit findings report, the clause 10.1 corrective-action records (recorded on the finding Issue items), the management review pack, and the approved clause 9.3 minutes and action register. Out of scope: the certification-body external audit and day-to-day control operation. No upstream workflow feeds this cycle and no single downstream workflow consumes its output; at close the cycle is archived on the Audit item as retained ISMS documented information, and carry-forward items re-enter the audit programme (the next PLANNED Audit item), the risk register, or the next review's inputs.
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SOC 2 Readiness & Evidence Collection
SOC 2 Readiness & Evidence Collection runs ON an already-opened Audit item — the SOC examination engagement record (audit_type readiness, or external_attestation) whose scope (report type and Type 1/Type 2), examination period (period_start/period_end), and CPA firm (external_firm) are already set. That Audit item is an INPUT: this workflow enriches it and attaches its run to it, never creating a duplicate engagement. It consumes the organization's own Control library — the Control items, framework tagged soc2/soc1 — and no upstream workflow feeds it. In scope: one SOC examination cycle end to end — map the Control library to each in-scope Trust Services criterion (Security always; Availability, Confidentiality, Processing Integrity, or Privacy only where a customer commitment requires it) and SOC 1 control objective, close readiness gaps, run the provided-by-client (PBC) evidence request list with QA, and coordinate the CPA firm through fieldwork and follow-ups. Named deliverables: the criteria-to-control mapping matrix and graded gap matrix, the owned PBC evidence request list, the QA'd evidence set, and the cross-referenced PBC response package — all attached to the anchor Audit and its workflow instance. Out of scope: the SOC report the CPA firm drafts and continuous control monitoring between examinations. No downstream workflow is declared; this run's next-cycle seed artifacts (the PBC list and control calendar) stay on the close step as the de facto handoff to the next examination.
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CSF 2.0 Profile & Maturity Assessment
Runs on an Audit item created for this assessment cycle (audit_type = readiness) — the CSF assessment engagement the workflow instance attaches to and enriches as it progresses (scope, period, rating, and report fields are written on that Audit item; every in-scope Control is linked to it so the controls-scoped profile is queryable). Build, against that boundary, a NIST CSF 2.0 Current Profile, a Target Profile, an organizational Tier rating, a subcategory gap analysis, and a CISO-ready remediation roadmap. The workflow originates on its own — scoping ingests prior CSF profiles and open POA&M (Issue) items as data, not as a named upstream handoff. In scope: rating the in-scope control set against the CSF 2.0 Core, setting target outcomes, assigning a Tier, and producing a prioritized roadmap. Out of scope: executing the remediation projects themselves and re-performing independent assurance testing. The named deliverable is the assessment package (profiles, gap analysis, Tier, posture report, roadmap, closure artifact), handed off to TWO downstream workflows that consume it rather than repeat the profiling: the Cybersecurity Assurance Review (always) and the AI Governance & Risk/Impact Assessment (only when AI systems fall inside the boundary).
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Public Content & External Sharing Authorization
Standing operator workflow for the public-posting and external-sharing authorization queue plus the quarterly permitted-without-authentication register and public-content exposure sweep, run per publication request and each quarter. Each operating cycle runs as one instance that enriches the existing UC-ACCESS-14 Control item in the control library (NIST 800-53 AC-14/AC-21/AC-22, family AC, preventive, quarterly) — it never creates a new control, and the workflow instance itself is the durable audit trail attached to that Control. In scope: public-facing systems (public website, support portal, developer documentation, status page, social channels) and external data shares governed by sharing agreements. Out of scope: authenticated internal content and access provisioning. Consumes each cycle: the living public-content and external-share register, the permitted-without-authentication register, and the active-sharing-agreements register (all pre-existing, refreshed cycle over cycle); the information-classification scheme (a Policy item, policy_type: standard); and the prior cycle's open carry-forward Issues. Named deliverables: the classified intake register, the authorization-verification log, the per-request publication dispositions, the refreshed permitted-without-authentication register, the quarterly exposure-sweep dashboard and findings report, and exposure corrective-action Issues linked to the Control. This control runs standalone — no upstream workflow feeds it and no downstream workflow consumes its output; the per-request authorization path and the quarterly sweep path are independent and both close in this instance.
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Change & Release Management (CAB)
Change & Release Management (CAB) as a decision-aware workflow that runs one recurring weekly instance against the EXISTING change-management ITGC Control item in the control library (domains: secure_configuration_change_management; mapped via Control.framework to nist-800-53, cobit-2019, iso-27001, and soc1, alongside the related UC-CONFIG-02/03, UC-ACCESS-16, and UC-SDLC-06/07/08/09 controls) — it enriches that control's operating evidence each cycle, it does not create the control. Upstream it consumes the open change-request queue and the emergency-change log exported from the ticketing system, plus the prior cycle's closure export as its carry-forward backlog. It covers change intake, security and risk impact analysis, environment segregation and configuration-baseline control, acceptance testing, the single CAB authorization gate, the emergency-change path, and controlled deployment with rollback and post-implementation verification — producing the prioritized CAB agenda and authorization packet, per-change risk-assessment memos, the environment-and-baseline verification memo, acceptance-testing summaries, the deployment-and-verification record, and the archived per-cycle evidence set. In scope: application, database, infrastructure, configuration, and procedure changes across development, test, and production environments. Out of scope: authoring the change itself — this workflow governs authorization and release, not development of the underlying code or configuration. Studio ships no native Change Request item type, so per-change records live as lines in step documents and in the workflow instance rather than as items. This is a terminal workflow with no downstream handoff: closure archives the cycle evidence set in place under retention, and the next cycle's intake reads this instance's closure export as its carry-forward source.
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Secure Development & Release Security Gate
Each run attaches as a workflow instance to the existing Control item for the secure-development/release-security-gate control (domains: secure_development_sdlc + vulnerability_patch_management) — enrich that Control, never create a duplicate; release runs and the quarterly checkpoint are separate instances on the same anchor Control. This workflow originates on its own artifacts (the release candidate, design artifacts, and the standing portfolio registers) and receives no upstream handoff package. Decision-aware: it branches on trigger type. In scope — for a release or major upgrade entering development, run security-and-privacy-by-design engineering, security test-plan execution, and runtime-hardening verification as parallel evidence streams, then resolve the release security disposition and assemble the release security evidence package with its acceptance-criteria index; for the quarterly portfolio checkpoint, run the vulnerability, patch, and end-of-life software review, publish the portfolio-health dashboard, and log corrective actions. Out of scope — the final production go/no-go, which the separate SDLC gate review workflow owns using the evidence package this workflow hands off to it. The release track and the quarterly track never force each other's steps.
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Resilient Architecture & Non-Persistence Operations
Standing operator workflow that runs each quarter against the existing NIST 800-53 resilient-architecture / non-persistence Control item in the control library (framework=nist-800-53, SI/SC family covering SI-14, SI-21, SI-22, SI-23, SC-25, SC-29, SC-36, frequency=quarterly, control_owner = Infrastructure Architecture Lead); the recurring instance attaches to and enriches that Control — it never creates a new control. In scope: non-persistent provisioning and information refresh, diverse sourcing and fragmentation of high-value data, and the resilient-architecture review of thin nodes, technology heterogeneity, and distributed processing and storage against current threats. It consumes no upstream workflow handoff; its inputs are the standing non-persistence and resilient-architecture inventories — the critical-information suppliers adopt the native Vendor type, while the infrastructure components, information stores, and fragmentation entries have no native item type and are held as register documents on the anchor Control — plus any open carryover Issue items from the prior cycle. Named deliverables: the refresh-and-attestation log, the sourcing-diversity and fragmentation-verification report, the thin-node / heterogeneity / distribution assessments, the combined resilience-posture dashboard and posture-review summary, and the adjustment-action register; exceptions, drift, and gaps normalize into Issue items linked to the Control. Out of scope: incident response itself — a suspected refresh-source compromise is contained and escalated to the incident-response team (an escalation, not a workflow handoff) — and application-layer change management. This is a terminal recurring cycle: close-and-archive exports the run as the durable operating record and seeds the next quarter's inputs.
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Supply-Chain Integrity & OPSEC Operations
Standing monthly operator workflow run against the existing supply-chain integrity Control item (UC-TPRM-07, frequency=monthly, domains=third_party_supply_chain_risk), with the OPSEC need-to-know Control (UC-TPRM-09) linked as the second in-scope control — enrich these existing Control items, never recreate them; the workflow instance attaches to the anchor Control as the durable operating record. Two concurrent workstreams. In scope: receipt-time tamper-evidence and authenticity inspection of critical systems and components, provenance and chain-of-custody upkeep, suspected-counterfeit disposition (each raised as a finding Issue linked to the anchor Control and the implicated Vendor) with inspector-training refresh where a lapse is found, and the OPSEC need-to-know review of the sensitive supply-chain information register with remediation of any overexposure. Named deliverables: the authenticated provenance and chain-of-custody register, the counterfeit-disposition cases, the OPSEC exposure-review worksheet, and the confirmed need-to-know-restricted disclosure footprint. No upstream workflow feeds this cycle — its inputs are the period's own receiving log and the sensitive supply-chain information register. This is a terminal standing control: procurement and vendor onboarding, contract-level third-party risk assessment, and facility physical security are out of scope, each handled by its own workflow; a substantiated counterfeit or compromised supplier is escalated to the third-party/vendor risk workflow rather than resolved here.
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Secure SDLC Phase-Gate Program
Operate the secure SDLC phase-gate program as a modular, decision-aware workflow where the accountable control owner chairs the intake, requirements, design, and build/release gates end to end. Each recurring operating cycle runs as a new workflow instance attached to the existing secure-SDLC phase-gate Control item (domains = secure_development_sdlc; framework = nist-800-53 / iso-27001 / cobit-2019 / nist-csf-2) — that Control already exists in the library, so the instance enriches it with this cycle's gate evidence rather than creating a duplicate control. In scope: chairing those four security-integrated lifecycle gates for every development initiative due this cycle, remediating held or blocked initiatives, and monitoring the process itself; it is self-initiating and consumes no upstream workflow package, and it seeds its own next cycle. Out of scope and distinct from it: the independent SDLC gate review's release-milestone verification, and the release-security-gate's technical testing practice.
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Outsourced & Critical-Component Development Oversight
Quarterly outsourced- and critical-component development oversight, run as a recurring operating cycle against the EXISTING Control item for outsourced/critical-component secure development (UC-SDLC-10 / UC-SDLC-11; domains secure_development_sdlc + third_party_supply_chain_risk; NIST 800-53 SA-20/SA-21/SA-23, ISO 27001 A.8.30) — the workflow enriches that Control, it never creates a duplicate. Each quarter it inventories the active outsourced/third-party development engagements (enriching the Vendor register), verifies contract secure-development / IP-ownership / audit-rights terms, traces deliverables to requirements with security-test evidence on file, screens critical-system developers before access, refreshes the register of components critical to security or mission, and re-tests the specialized-development rationale and assurance evidence. It consumes the prior cycle's carry-forward Issue items (open corrective actions, contract renewals, components due for rationale review) and the archived prior workflow instance's registers; every gap it surfaces becomes an Issue linked to the anchor Control, giving one queryable corrective-action population. In scope: active outsourced and third-party development engagements and the register of components critical to security or mission; out of scope: internal-only development with no third-party contributor and general procurement risk unrelated to development. Self-terminating: no downstream workflow consumes this cycle's output — corrective actions are tracked to closure within this workflow and carried forward to the next quarter.
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Technology Lifecycle & Capacity Review
Standing operator workflow for the quarterly solution-asset lifecycle review and the availability/capacity planning cycle, from asset reconciliation and end-of-support disposition through capacity monitoring, corrective planning, and prior-period target validation. Each quarterly run is a recurring workflow instance attached to the EXISTING technology-lifecycle-and-capacity-management Control item (UC-SDLC-12/13; frequency = quarterly; control_owner = the IT control owner) — the instance enriches that standing Control and its linked records, never a fresh duplicate; the governing COBIT 2019 / NIST 800-53 mapping rides on Control.framework. In scope: the in-scope solutions, applications, and supporting infrastructure components (the services they run are Process items) with named owners, and the services carrying agreed availability and capacity (SLA/SLO) targets. Out of scope: the projects that execute an approved replacement or upgrade (each tracked externally as its own project item) and live incident response for availability outages. Named deliverables: the reconciled solution-asset register and optimization actions, the ranked end-of-support exposure list and disposition, funded replacement/upgrade plans (Issue items) and compensating-control records (Control items) with signed risk acceptances (policy_exception Issues carrying exception_expiry_date, on treatment: accept Risks), the capacity/availability dashboard and corrective plans, and the validated prior-period target package. No upstream workflow feeds this cycle and there is no separate downstream workflow to hand off to — the still-open replacement/upgrade plans, capacity corrective plans, and risk-acceptance expiries (their existing Issue and Risk items, linked to the anchor Control) carry forward as explicit inputs to the next quarterly run of this same workflow.
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AI Operations Monitoring & Incident Response
Each monthly instance runs against the existing Control item for AI operations monitoring and incident response (framework eu-ai-act + iso-42001; domains ai_governance / logging_monitoring_detection / incident_management_response; frequency monthly; control_owner AI Operations Manager) — the run enriches that Control's execution history and is its evidence of operation, it never creates a duplicate. The decision-aware cycle covers event-logging and retention verification, alert resolution, human-oversight confirmation, AI-use screening against legal prohibitions, and AI concern-and-incident triage through required communications and regulator reporting, consolidating all five streams into a named cycle report and cycle dashboard while every residual gap is booked as an Issue (source: management_identified) linked to the anchor Control. In scope: every deployed production, limited-risk, and high-risk AI system under the EU AI Act, its event-log sources and monitoring dashboards, the human-oversight roster, the AI use inventory, and the AI concern-reporting channels. Out of scope: pre-deployment model development and conformity assessment, and third-party/vendor AI due diligence, which are governed by their own workflows. This cycle consumes no upstream workflow handoff; it hands off only to the next monthly run of itself, seeding it with the open carry-forward Issues (corrective actions, in-flight blocks, incident follow-ups) that the cycle-metrics-and-report step links to the anchor Control so next month can query them as its prior-cycle input.
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AI Guardrail Configuration & Agent Permission Review
Each monthly or release-triggered instance runs against the existing Control item for AI guardrail configuration and agent permission review (framework aiuc-1 + iso-42001 + eu-ai-act; frequency monthly and per release; control_owner AI Platform Security Lead) — the run enriches that Control's execution history and is its evidence of operation, never a duplicate. The decision-aware cycle confirms the in-scope agent population and baseline; reviews input defenses and endpoint limits, tool allow-lists, permissions and sandboxing, output filters and grounding, misuse refusals, secrets redaction, and secure-code-generation defaults; decides on agent permission scope and on guardrail drift with a remediation branch for each; and consolidates the results into a signed guardrail attestation with cycle metrics and owned actions, booking every residual gap as an Issue (source: management_identified) linked to the anchor Control. In scope: every production AI agent and inference endpoint, its guardrail configuration, tool-call and detection logs, and configuration artifacts. Out of scope: model development, pre-deployment evaluation, and vendor AI due diligence, which have their own workflows. The cycle hands off only to its next instance through the carry-forward Issues that the guardrail attestation step links to the anchor Control.
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Quarterly Third-Party AI Evaluation Cycle
Each quarterly instance runs against the existing Control item for independent third-party AI evaluation (framework aiuc-1 + iso-42001 + eu-ai-act; domains ai_governance; frequency quarterly; control_owner AI Product Lead) — the run enriches that Control's execution history and is its evidence of operation, it never creates a duplicate Control; the one record it does create is a per-quarter Audit item (audit_type: it_audit) for the evaluator engagement. The decision-aware cycle confirms the quarter's in-scope systems and risk taxonomy, engages an independent evaluator with the test plan, categories, and pass thresholds fixed in advance, provisions contained test access, triages every finding by category and severity against the tested control, routes failed thresholds through remediation and evaluator retest, gates acceptance of the evaluator report, publishes the accepted evidence (trust-portal summary, customer-facing attestation, evidence register), and tunes guardrails from the findings. In scope: every in-scope AI system and agent under the AIUC-1 program and its six mandatory third-party test categories (B001 adversarial robustness, C010 harmful outputs, C011 out-of-scope outputs, C012 agent-specific risk, D002 hallucinations, D004 tool calls). Out of scope: internal red-teaming, pre-release model evaluation, and vendor AI due diligence, which run in their own workflows. It hands off only to the next quarterly run of itself, seeding it with the open carry-forward finding Issues that the publish-evidence-and-tune-guardrails step links to the anchor Control.
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Change Request, Approval & Migration
Runs on the existing system item. Move a system change from request through independent testing and approval to production migration, evidencing developer-migrator segregation. Deliver the reviewed result and open actions to the responsible register owner and the named companion procedure.
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Emergency Change
Runs on the existing system item. Record an emergency change that bypassed normal change control, evidence the elevated access used, and retrospectively test whether the bypass was justified. Deliver the reviewed result and open actions to the responsible register owner and the named companion procedure.
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New System Implementation (SDLC)
Runs on the existing system item. Take a new system from control requirements through testing and acceptance to an evidenced go-live readiness decision. Deliver the reviewed result and open actions to the responsible register owner and the named companion procedure.
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Security Control Assessment & POA&M Remediation
Run this assessment on the EXISTING Audit item for the engagement (audit_type: it_audit or compliance) — enrich that record, never create a duplicate: Audit.scope carries the authorization boundary and Audit.period_start/period_end the assessment window. Consumes, from the upstream SSP-development / system-categorization effort, the approved System Security Plan (SSP), the FIPS 199 system categorization, and the tailored NIST 800-53 baseline (existing Control items, framework: nist-800-53). Assess each in-scope control with 800-53A examine/interview/test methods, record satisfied / other-than-satisfied determinations, open a POA&M Issue for every gap, re-validate remediation, and issue the Security Assessment Report (SAR). In scope: control assessment, determinations, the POA&M lifecycle, and the SAR for the authorization boundary agreed at kickoff. Out of scope: the authorization (ATO) decision itself and the steady-state continuous-monitoring cadence. On completion, the frozen SAR and POA&M package are handed to the NIST RMF System Authorization (ATO) Cycle.
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ISO 27001 Stage 1 ISMS Documentation Review
Certification-body Stage 1 review of the ISMS against ISO/IEC 27001:2022 clauses 4–10: context and leadership, planning and support, operation, and performance evaluation with improvement - walking each clause group against the governing documents and the operating processes that carry it, and concluding Stage 2 readiness with dual sign-off. Stage 1 documentation and readiness review for ISO/IEC 27001:2022 clauses 4–10, conducted under the engagement methodology. It informs Stage 2 planning and does not issue a certification decision. Attach this workflow to the existing audit engagement item; retain evidence and conclusions on its workflow steps.
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ISO 27001 Stage 2 Annex A Controls Audit
Attach to the existing Audit engagement, owned by Internal Audit, using its approved Statement of Applicability, risk treatment plan, scope, review period and operating evidence; produce the Stage 2 Annex A Controls Audit report, four signed theme conclusions and finding register for the engagement and remediation owners. Apply the approved Statement of Applicability to ISO/IEC 27001:2022 Annex A.5.1–A.5.37, A.6.1–A.6.8, A.7.1–A.7.14 and A.8.1–A.8.34; document each exclusion and assess direct and inherited responsibilities. This Annex A assessment contributes to the engagement and does not independently establish full ISMS conformity or issue certification. Stage 1 and readiness remain separate workflows; any certification decision remains with the authorized certification body.
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SOC 2 Privacy Criteria Assessment
Design-readiness review of the full SOC 2 privacy series: notice and choice, collection through disposal, data subject access and disclosure, and data quality with dispute monitoring (P1.1–P8.1), concluded with dual sign-off on the criteria-bearing final assessment. Design-readiness assessment limited to the listed SOC 2 criteria. Evidence may include operating examples to assess the design; this module does not provide a SOC 2 Type II opinion. Attach this workflow to the existing audit engagement item; retain evidence and conclusions on its workflow steps.
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SOC 2 Type II Interim Testing
Interim fieldwork for the Type II examination: cycle walkthroughs, design assessment against the Trust Services Criteria, the first operating-effectiveness testing wave over the agreed interim evidence window, and exception triage feeding remediation and retest planning before the period closes. Interim SOC 2 Type II fieldwork for the listed control selections and agreed interim window. Later-period testing and the service auditor's independent opinion remain outside this module. Attach this workflow to the existing audit engagement item; retain evidence and conclusions on its workflow steps.
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GCP Physical and Environmental Subservice Reliance
Evaluate GCP assurance-report scope, physical and environmental controls, exceptions and user responsibilities; record a bounded reliance decision rather than claiming to operate provider facilities. This customer-side review relies on relevant independent reports, disclosed provider information and customer implementation evidence. It does not operate or certify GCP facility access or environmental safeguards. Preserve each report's permitted-use restrictions.
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System ITGC Operation
Operate and evidence the IT general controls on a system: authentication and password configuration, access provisioning and deprovisioning, the periodic user access review, change management (testing, sign-off, logs), and subservice SOC report reliance.
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Domain Oversight and Management Review
Quarterly management review of one process area - the process area's own oversight run. The domain owner reviews the registers the domain operates (systems, tenants, audits), the health and evidence of the operating-process runs, the domain's risks, issues and metrics, and the operation of its controls, then records direction and a dual sign-off. Instantiated once per process area; the operating processes keep their own runs.
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Risk Assessment and Treatment Review
Runs on the existing risk item. Assess a risk against current context and evidence, select a supported treatment response, and approve a traceable review record. Deliver the reviewed result and open actions to the responsible register owner and the named companion procedure.
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SOC 2 Reporting and Management Assertion
Reporting close for the Type II examination: drafting and validating the system description under the carve-out method, preparing the management assertion, reviewing complementary user entity controls and subservice reliance, and the dual-approval close of the examination file at the agreed period end. Management-owned SOC 2 Type II reporting support: system description, management assertion, CUECs, subservice reliance, and auditee file closure. The independent service auditor retains responsibility for examination conclusions and the CPA opinion. Attach this workflow to the existing SOC 2 evidence or reporting process item; retain evidence and conclusions on its workflow steps.
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System and Third-Party Risk Review
Runs on the existing system item. Review system and provider dependencies, assess current risk and control evidence, select response actions, and approve the review record. Deliver the reviewed result and open actions to the responsible register owner and the named companion procedure.
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Vendor Risk Assessment and Disposition
Runs on one existing System with vendor=true. Uses the supplier record, contracts, assurance/CUECs, access and continuity evidence to produce a reviewed vendor risk assessment and authorized disposition for the business/risk owner and readiness evidence consumers. Two checkpoints retain expert challenge and the owner’s choice of conditions, treatment and monitoring; executor work is recorded in step results and documents, with no collection forms or runtime branches.
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ISO/IEC 42001 AI Management System Internal Audit
An independent internal-audit cycle for the AI management system: set scope and criteria, test governance, risk, documentation, operations, value-chain controls, and reporting, then issue findings and an evidence-backed conclusion. The audit supports management improvement and assurance; it does not certify conformity.
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AI Governance & Risk/Impact Assessment
Assess a single AI system end to end under ISO/IEC 42001 (AIMS) and the NIST AI RMF: govern and register the system, map context and risks, measure risks and impacts, manage treatment, produce transparency artifacts, and authorize deployment with monitoring. The instance attaches to an Audit item created for this assessment cycle (audit_type compliance, or advisory for a pre-deployment review); because Studio has no native AI System type, the system under assessment is named in that Audit's scope and its lifecycle/EU-AI-Act detail lives in the scoping memo and step documents. In scope: one named AI system or use case and its lifecycle risk posture. Out of scope: enterprise-wide AI policy authoring and detailed EU AI Act legal obligation mapping, which are consumed as an input handoff package from the EU AI Act Obligation Impact Analysis workflow. The named deliverable is the approved AI assessment package (executive summary plus recommended governance decision), backed by the AI risk register (Risk items, category ai_governance), the model/system card and AI impact-assessment record, and a deployment authorization with live drift/fairness monitoring; approved outputs hand off to Quarterly Board & Audit-Committee GRC Reporting.
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Risk Appetite Definition & Board Reporting
Define enterprise risk appetite statements, tolerances, and KRIs, secure executive and board approval, monitor actuals against tolerances, and report the appetite position to the board. Runs as a standalone recurring instance per appetite cycle (typically annual): appetite spans the whole Risk register rather than a single item, so the register, tolerance and KRI matrix, monitoring workbook, and reporting pack attach to the workflow instance's steps as the versioned documents of record, with the existing Risk items as the linked reference data and KRI breaches recorded as Issue items (issue_type: exception, linked to their Risk). In scope: appetite-statement definition, tolerance and KRI design, executive validation, board approval, ongoing monitoring, and ERM board reporting. Out of scope: the enterprise-wide risk identification and scoring that produces the risk universe, and the assembly of the full quarterly board deck. Consumes the risk-assessment handoff package from the Enterprise Risk Assessment & Portfolio Oversight Cycle (the risk universe as Risk items plus inherent and residual ratings) rather than re-deriving it, and hands the board-approved appetite package to the Quarterly Board & Audit-Committee GRC Reporting workflow.
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Third-Party Vendor Risk Lifecycle
Operate the third-party vendor risk lifecycle end to end: scope and tier the vendor population, gather and analyze assurance evidence (SOC reports and CUECs, plus C-SCRM controls), embed contractual protections, enroll ongoing monitoring, and reach a governed disposition and approval. The vendor register IS the set of Vendor items — tiered by criticality (tier) and data exposure (data_classification), owned (business_owner, risk_owner), and driven by reassessment_cadence with last/next assessment dates and monitoring_status; each assessment cycle runs as one workflow instance over that register. In scope: vendor and supplier third-party risk assessment, onboarding controls, and periodic reassessment. Out of scope: procurement sourcing and commercial negotiation, and the deeper fieldwork of a Third-Party Vendor Assurance Engagement, to which the approved package is handed off. This workflow is self-initiating and consumes no upstream workflow package.
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IT Governance Objective Review (COBIT)
Periodic review of selected COBIT 2019 governance and management objectives, run per cycle on an Audit item (audit_type: it_audit; scope = the in-scope objectives; period_start/period_end = the assessment cycle) that the workflow instance attaches to and archives at close. Each in-scope COBIT objective is a Process item (process_type: it_general_control) linked to that Audit, and the review produces named deliverables against it: an evidence register and pre-scored capability sheet, a signed capability profile, a gap table with the benchmark decision, a committed improvement roadmap of Issue initiatives, and the governance board report and dashboard. In scope: the COBIT 2019 objectives selected for this cycle, each with a justified 0-5 target capability level, a named accountable owner, and the review cadence; out of scope: objectives explicitly excluded with recorded rationale. Self-originating: its scope sheet and target profile are supplied as workflow inputs, and it hands off to no distinct downstream workflow — the carry-forward improvement Issues and the archived instance seed its own next cycle.
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Risk & Resilience Framework Governance
Risk & Resilience Framework Governance as a decision-aware workflow. The instance runs on the "Enterprise Risk Management Framework" Process item (process_type: business_process, process_owner = the framework owner, frequency: annual) - created on the first cycle at "Design core ERM framework" and enriched every cycle thereafter, never duplicated; that Process item is the governance register entry, and each governance cycle runs as a workflow instance attached to it, so the at-least-annual cadence is provable from one item's instance history. Working from the organization's context and the ISO 31000 / COSO ERM / DORA reference models - no upstream workflow package feeds it, because this workflow establishes the governance layer - it establishes or refreshes the enterprise risk management framework, extends it for ICT operational resilience and regulated technologies, secures management-body approval and budget, drives implementation across the organization, and runs the at-least-annual review, filling the governance layer the risk-cycle workflows run inside but never establish. The named deliverable is the approved risk & resilience framework package (core ERM design + ICT operational-resilience (DORA) extension + any regulated-technology lifecycle extension), archived at close as durable governance evidence. In scope: the enterprise risk framework, its always-in-scope ICT operational-resilience (DORA) extension, and any regulated-technology (e.g. high-risk AI) extensions to be evaluated. Out of scope: executing the individual risk-cycle workflows (identify / assess / treat / monitor) that run inside this framework - this workflow governs them but does not perform them, and consumes no upstream workflow package. Downstream, the archived framework enables those risk-cycle workflows, which reference it as their governing baseline (the relationship is real but not modeled as a node).
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Combined Assurance Mapping
Combined Assurance Mapping as a decision-aware workflow. Each cycle runs as one workflow instance attached to an Audit item created for the cycle (audit_type: advisory, scope = the combined-assurance mapping scope for the period, period_start/period_end = the cycle period) — no other Studio type represents an assurance-coordination cycle, so the workflow enriches that Audit item rather than any pre-existing engagement. In scope: mapping assurance coverage across the Three Lines of Defense for the confirmed risk universe and entities this cycle — cataloging assurance providers, mapping their coverage onto the risk universe, assessing reliance, identifying gaps and duplication, coordinating coverage plans, publishing the combined assurance map, and preparing audit-committee reporting inputs. Out of scope: performing the underlying assurance engagements themselves (owned by internal audit, second-line functions, and external providers) and any risk, entity, or provider not named in this cycle's confirmed scope. It consumes the risk universe and residual positions (Risk items with their residual_rating and treatment) from the upstream Enterprise Risk Assessment & Portfolio Oversight Cycle and hands its named deliverables — the published combined assurance map, the reliance conclusions, and the gap action plans — to the downstream Quarterly Board & Audit-Committee GRC Reporting workflow rather than duplicating repeated work.
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Enterprise Risk Treatment Operations Cycle
Enterprise Risk Treatment Operations Cycle as a decision-aware workflow: it runs the documented risk methodology each cycle - identification and analysis of risks and opportunities, evaluation and prioritization against risk criteria, treatment selection and tracking for risks exceeding tolerance with residual re-evaluation, and risk-register and portfolio reporting to management and the board - triggering dynamic reassessment when internal or external change shifts the risk profile. This cycle has no anchor item of its own: the enterprise risk register it maintains IS the Risk item population, and the workflow instance is the cycle record and durable audit trail. In scope: entity-level and process-level risk across the enterprise, explicitly including cybersecurity, privacy, and financial-reporting risk alongside operational and strategic risk and opportunity. Out of scope: detailed control design and testing, which downstream control workflows own - a mitigate or share/transfer plan that creates or strengthens a control hands that work off to those workflows, which anchor on the affected Control items. This cycle has no upstream workflow feeding it; its starting inputs are the documented methodology, the consistent likelihood/impact scoring scales, the board-approved risk criteria and appetite/tolerance statements, and the risk-acceptance delegation matrix - all carried as Policy items - plus the existing control, insurance and transfer information (Control items and step documents) and the prior cycle's Risk register. Named deliverables: the maintained enterprise risk register (the Risk items), the prioritized risk heat-map dashboard, and the management and board portfolio report package.
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Risk Communication, Reporting & Performance Review
Risk Communication, Reporting & Performance Review as a decision-aware workflow that runs each quarter or on an out-of-cycle significant matter. Because none of the eight Studio item types represents the ERM reporting cycle itself, the workflow instance IS the durable record: its named deliverables - the tiered internal and external risk, control and performance reporting package, the event-driven report on a significant matter, the management-signed risk-and-control performance-review pack, and the tracked improvement actions - attach to its steps, its item-level writes land on the existing Risk, Control, and Issue items (improvement actions are tracked as Issues with source: self_assessment); control-testing results are read from SOX testing workflows hosted directly on the relevant Controls; and the risk-management framework is read as its Policy item (policy_type: charter) for the evaluation baseline and the suppliers and third parties consulted as their Vendor items. In scope: stakeholder consultation across every risk-process step (identification, assessment, response, monitoring) including suppliers and third parties; the cadenced internal and external reporting package; event-driven reporting on significant matters; the periodic review of risk-management and internal-control performance against the framework's design intent with accountable management; and converting lessons into owned, tracked improvement actions. Out of scope: running the underlying risk assessments, control testing, or business-performance measurement themselves - this workflow consumes their results as inputs. It starts on its own trigger (the quarterly cadence or a significant event) and has no upstream feeder workflow and no named downstream handoff workflow; each run's close-and-archive leaves the stakeholder, risk, and improvement registers current - the informal handoff both to the next cycle of this workflow and to the downstream risk-assessment and control-testing workflows whose results this cycle consumes.
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Vendor Due Diligence & Contracting Gate
Vendor Due Diligence & Contracting Gate as a modular, decision-aware workflow. Anchor: the vendor's register entry — the Vendor item (slug: vendor). For a net-new engagement the gate creates the Vendor item and populates its tier, data_classification, business_owner, and risk_owner; for a renewal it enriches the existing Vendor item rather than duplicating it (enrich, never recreate). This is the first-line pre-contract gate the vendor-management office runs for every new engagement or renewal - tiering criticality, running proportionate due diligence into the vendor due-diligence report, documenting the risk-acceptance decision and any risk-reducing sourcing conditions, binding the contract to required security, privacy, and regulatory clauses, authorizing the specific information exchange before access begins, and - where personal data is involved - obtaining the signed written privacy commitments and scheduling compliance monitoring and incident-response routing. In scope: pre-contract due diligence, risk acceptance, and contract execution for one vendor engagement, ending with the Vendor item enrolled in monitoring (Vendor.monitoring_status = enrolled). There is no upstream workflow — the engagement trigger (a new prospective vendor or a renewal) is its own entry point. Downstream handoff: the archived gate record exported at close is the handoff package the ongoing third-party risk monitoring / vendor oversight lifecycle picks up to sustain the scheduled compliance checks and incident routing; that linkage is a prose handoff of live controls on the same Vendor item, not a triggered downstream node.
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Third-Party Risk Program & Vendor Oversight Cycle
A standing quarterly cycle the vendor-management office runs as control owner. The workflow instance is a recurring run attached to the EXISTING Process item "Third-Party / Vendor Risk Management" (process_type: operational, process_owner: Vendor Risk Program Lead, frequency: quarterly), linked to the Control items for the unified controls it operates (UC-TPRM-01/04/05/08) — it enriches that standing program, never recreating it. It consumes no upstream workflow handoff: each run is self-feeding, drawing its criteria and prior state from the program's own standing artifacts — the SCRM plan, third-party risk policy, and program strategy held as Policy items; the criticality-tiered Vendor register (Vendor items); and the prior cycle instance's step documents (the DORA Article 28(3) register of information and the ICT concentration-risk view). In scope: reaffirming or revising the governing Policy items; re-tiering the Vendor register; refreshing the DORA Article 28(3) register of information and the concentration-risk view (a dashboard over the Vendor items); verifying critical-provider exit strategies; executing this cycle's tier-based reassessments and driving findings to tracked third-party Risk items (remediation or risk-committee escalation); confirming external and cloud service provider oversight; and verifying critical suppliers carry live incident-notification coverage. Named deliverables: the refreshed criticality-tiered Vendor register, the updated DORA Article 28(3) register of information, the recomputed ICT concentration-risk view, the exit-readiness summary, this cycle's tracked Risk items, and the archived cycle evidence package on the workflow instance. Terminal at close-and-archive with no downstream handoff — open or escalated vendor risks persist as Risk items in the risk register. Out of scope and handled by separate workflows: the continuous monitor-line vendor lifecycle and the per-engagement due-diligence gate for onboarding a new vendor.
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AI Governance Framework, Roles & Obligations Review
AI Governance Framework, Roles & Obligations Review as a modular, decision-aware workflow. Each quarterly instance runs on the existing "AI Governance Program" Process item (process_type operational, quarterly, process_owner = AI Governance Officer) and enriches its standing registers — it never recreates them. It keeps the AI policy current and republished, refreshes the RACI and competency records, maintains each AI system's resource and dependency inventory, and walks the value-chain obligations register so every provider and deployer duty traces to an owner and evidence. Consumes at launch: the cycle trigger (interval, folded-in annual re-approval, or a regulatory/technology change) and prior-cycle registers — the AI policy as a Policy item (framework iso-42001 + eu-ai-act, domains ai_governance), the obligations register as EU AI Act / ISO 42001 Control items (control_owner = obligation owner), plus the in-scope AI system list, the RACI and competency register, and the per-system resource inventory, which have no native item type and travel as versioned step documents. Deliverables: the republished AI policy version, the refreshed RACI and competency register, the current resource and dependency inventory, the walked obligations register, and a four-area cycle evidence package. In scope: those four governance registers for the AI systems in boundary this quarter; out of scope: the AI systems' own development, risk assessment, and control testing. Terminal — no downstream workflow consumes the output; the cycle closes to its own archived workflow record, linked back to the anchor Process item.
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Risk & Control Self-Assessment (RCSA) Program
Risk & Control Self-Assessment (RCSA) Program as a modular, decision-aware workflow. Each wave runs on its own Audit item — created per wave (audit_type: operational; period_start/period_end = the wave window; report_date = the risk-committee date) — with the workflow instance attached to that item and the wave's questionnaires, attested returns, and calibration record kept inside the run. Each wave rebuilds the assessment universe from the existing Process, Risk, and Control items and their owners (enriching them, never recreating them), issues rating questionnaires to named control and process owners, collects attested self-assessments with structured exception capture, chases completeness, subjects the results to second-line challenge and calibration, aggregates a residual-risk view across units, updates the risk register's residual ratings, and routes self-identified issues to remediation and exceptions to time-bound acceptance before the results reach the risk committee. In scope: first-line self-assessment of in-scope business units and shared functions against their own risks and controls. Out of scope: independent testing/audit of those controls, and the remediation and formal risk-acceptance of what the wave surfaces, which are handed off downstream to the Finding Remediation & Action-Plan Monitoring (deficiencies), Policy Exception & Risk Acceptance (risk-acceptances/waivers), and Quarterly Board & Audit-Committee GRC Reporting (the wave report) workflows.
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Regulatory Exam & External Audit Management
Manage a live regulator examination or external audit end to end — from notification intake through request fulfillment, QC’d evidence release, fieldwork support, preliminary-findings response, and commitment closure. Runs on an Audit engagement item created per exam (audit_type = regulatory_exam, or external_attestation for an external audit); the workflow instance attaches to that Audit anchor, and preliminary findings and their corrective-action commitments become linked Issue items. No upstream workflow feeds this — it is triggered by the exam or audit notification itself. In scope: coordinating examiner requests, controlled evidence release, and management responses for a single exam or audit engagement. Out of scope: remediating the underlying control gaps — the findings and committed corrective actions hand off to Finding Remediation & Action-Plan Monitoring — and standing up new obligations surfaced by the exam, which hand off to Regulatory Horizon Scanning & Triage and Regulatory Obligation Implementation.
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AI Service Data Policy & Quality Management Cycle
AI Service Data Policy & Quality Management Cycle as a modular, decision-aware workflow. Each annual instance — and each semiannual regulatory-compliance checkpoint — runs on the existing "AI Service Data Policy & Quality Management" Process item (process_type operational, process_owner = AI Product Owner) and enriches its standing records; it never recreates them. Run by the AI Governance Lead (second line) and owned by the AI Product Owner (first line), it refreshes the customer-facing input data policy and output data policy, collects customer acknowledgements where the changes are material, reviews the AI quality management system for effectiveness, and refreshes the regulatory compliance documentation and obligations register shared with customers. Consumes at launch: the cycle trigger and mode, the in-scope AI service list with each service's value-chain role (a step document — there is no AI System item type), both data policies and the quality manual and procedures as Policy items, and the obligations register as Control items. Deliverables: the published policy versions with their acknowledgement records, the quality management system review record, the refreshed compliance statement and obligations register, the corrective-action log, and an indexed cycle evidence pack. Out of scope: the AI services' own development, risk assessment, and control testing, and fulfilment of individual customer data requests. Terminal — the cycle closes to its own archived workflow record, linked back to the anchor Process item.
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Emerging Risk & Horizon Scan
Runs on the existing risk item. Scan the forward horizon for signals of an emerging exposure, assess plausibility and velocity, and decide whether it enters the register or stays on the watchlist. Deliver the reviewed result and open actions to the responsible register owner and the named companion procedure.
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Enterprise Risk Register Lifecycle
Enterprise Risk Register Lifecycle as a decision-aware workflow. This is a standalone recurring instance (quarterly or annual) that runs against the existing Risk item population — the enterprise risk register itself — enriching those Risk items in place rather than recreating a register: per-risk results are written onto the individual Risk items, and cycle-level deliverables attach to the workflow instance's steps. In scope: maintaining the register across the confirmed entities, business units, and risk-taxonomy categories for this cycle — intake and deduplication of new risks, Three-Lines ownership, control and assurance mapping, KRIs, periodic review and escalation, and retirement. Out of scope: any entity, unit, or category not named in this cycle's confirmed scope. It consumes the candidate-risk handoff package from the upstream Risk Register Intake workflow and hands its maintained register, residual positions, and escalations to two downstream workflows — Enterprise Risk Assessment & Portfolio Oversight Cycle (the maintained register, the concentration and correlation flags, and the residual positions) and Risk Appetite Definition & Board Reporting (the above-appetite entries, the escalations, and the acceptances) — rather than duplicating repeated work.
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Personal Data Quality & De-identification
Personal Data Quality & De-identification runs as a recurring operate cycle attached to the existing Control for personal-data quality & de-identification (framework gdpr/hipaa/ccpa/nist-800-53, domain data_protection_privacy): each period is a workflow instance that enriches that Control's operating record — never a new Control. The cycle consumes the data map / RoPA, the data-quality ruleset and retention schedule (Policy items), the individual correction-request queue, the disclosure log and the recipient/processor Vendor register, and the de-identification policy; it checks PII accuracy, relevance, timeliness, and completeness, corrects or deletes failing records and notifies downstream recipients under GDPR Art. 19, adjudicates individual correction requests within statutory deadlines, and de-identifies datasets that do not require full identifiers. It produces the exception register (exception Issue items), the corrected-and-deleted set, the recipient correction/deletion notices, and the de-identification and residual-risk reports; systemic findings hand off to the internal privacy backlog as Issue items. This is a terminal operate cycle — the archived, regulator-ready cycle record is the deliverable, not a handoff package for a named downstream workflow.
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Privacy Safeguards & Notice Management
Privacy Safeguards & Notice Management as a decision-aware cycle that runs as a workflow instance attached to the standing "Privacy Program" Process item (process_type: business_process, process_owner: the privacy officer) — one archived instance per period is the cycle file. Each run refreshes the privacy requirements inventory, confirms PII-protection accountability over the PII stores (modeled as processing-activity Process items), verifies administrative, technical, and physical safeguards against data volume and sensitivity (the safeguard set is the Control items with control_category administrative|technical|physical and framework gdpr|ccpa|hipaa protecting each store), remediates gaps as tracked Issues, and authors, publishes, and version-retains the privacy notices and registrations (modeled as Policy items) kept accurate to actual processing. In scope: the periodic privacy program cycle over the confirmed PII stores, their safeguard control set, and the published notice and registration population; every run reconciles the affected notices to actual processing — a cycle that skips notice reconciliation is a different workflow. No upstream or downstream workflow feeds or consumes this cycle; its inputs are the organization's own privacy program records, and open remediation, treatment expiries, and deferred drift carry forward as open Issues that seed the next cycle's intake.
workflow · Context
AI System Development, Data & Deployment Gate
Runs as a standalone per-release gate cycle — one workflow instance per new AI system or substantial modification. The schema has no native AI System item type, so the instance links (Item relationship) to the existing Control items it operates (UC-AI-04/05/06/07/09; framework eu-ai-act|iso-42001, domains ai_governance) and to the ai_governance Risk it mitigates, and all cycle evidence attaches to its steps. It consumes the AI system inventory profile and the enterprise responsible-AI objectives (Policy items) upstream; the release board then approves those objectives and the per-system requirements before build, governs training, validation, and test data with bias mitigation, executes the pre-deployment impact assessment and EU AI Act risk classification, applies the high-risk conformity obligations where they trigger, assembles Annex IV-grade technical documentation, and records verification-and-validation results and the deployment sign-off. Named deliverables: the risk-classification decision, the pre-deployment impact-assessment report, the Annex IV technical-documentation package, the verification-and-validation report, and the recorded sign-off. Retraining and substantial changes re-enter the same gate as a fresh instance (a self-loop, no downstream handoff).
workflow · Context
AI Transparency & Value-Chain Communications
Each quarterly instance runs against the existing "AI Transparency & Value-Chain Communications" Process item (process_type: business_process, frequency: quarterly), enriching it rather than recreating it, and links to the existing Control items UC-AI-10 and UC-AI-14 (framework: iso-42001 + eu-ai-act, domains: ai_governance) that the cycle executes. A recurring operate cycle that keeps AI system documentation, interaction disclosures, and content marking current with system changes, retains distribution evidence, evaluates AI suppliers (Vendor items) against responsible-AI requirements, and reviews customer needs and communications; the transparency policy and content-marking standard it enforces are Policy items. It consumes no upstream handoff package (a self-originating quarterly cadence) and terminates in no downstream workflow — systemic findings and carry-forwards land as Issue items in the AI governance backlog. In scope: every AI system in production or customer-facing use and every AI supplier providing an AI system, model component, training data, or inference service; out of scope: risk-classification and conformity assessment itself — purpose or risk-class drift is routed to the EU AI Act Impact Analysis workflow (as an observation Issue referencing it) rather than resolved in this cycle.
workflow · Context
GPAI Model Provider Compliance Cycle
Recurring operate cycle that fulfills Article 53 general-purpose AI model provider duties every release and quarterly refresh, and layers in Article 55 systemic-risk duties for models designated as posing systemic risk. The cycle runs as one Workflow instance on the existing Process item "GPAI model governance / provider compliance" (process_type: business_process, process_owner: Head of Model Governance) — enriched each cycle, never recreated — and links to the Control item implementing UC-AI-15. It consumes the prior cycle's archived instance on the same Process anchor (the per-model scope/exemption register and the prior version-of-record documentation carry forward), and produces the archived, authority-producible provider-obligation cycle record as its named deliverable. Note: the Studio catalog has no AI-Model item type, so per-model artifacts — scope, open-source-exemption determination, documentation and pack versions, publication records — live as version-stamped step documents and on the Workflow instance rather than on a model item, with the per-model scope register carried as a document on the anchor Process item. In scope: every general-purpose AI model this provider places on the EU market, scoped per model to Article 53 alone or Article 53 plus Article 55, with per-model provider-status and Article 53(2) open-source-exemption determinations carried on that scope register. Out of scope: the downstream integrator's own AI-system risk-class obligations, which each deployer operates separately; there is no named downstream workflow this cycle hands off to.
workflow · Context
Regulatory Change Intake & Impact Assessment
Runs on the existing requirement item. Validate a new or amended external obligation against its authoritative source, determine applicability, and assess the impact on controls, policies, processes and systems. Deliver the reviewed result and open actions to the responsible register owner and the named companion procedure.
workflow · Context
EU AI Act Obligation Impact Analysis
EU AI Act Obligation Impact Analysis runs on a compliance Audit item created at intake (`audit_type: compliance`, `scope` = the fixed entities/AI-systems/markets boundary) — the "impact-analysis item" the whole run enriches and closes. It parses in-scope EU AI Act provisions, classifies affected AI use cases by operator role and risk tier, crosswalks obligations to existing Control items, rates conformity gaps as Issue items, and routes high-risk gaps to the AIMS (ISO/IEC 42001 AI management system). Consumes the horizon-scanning handoff package from Regulatory Horizon Scanning & Triage. Named deliverables: the locked workplan, the obligation register (the durable run-to-run workbook), the per-use-case classification set, the obligation-to-control crosswalk, the ranked conformity-exposure list, and the version-pinned final analysis package. Hands its approved gap-and-action package to Regulatory Obligation Implementation. In scope: obligation parsing, use-case classification, control crosswalk, and gap rating for the entities, AI systems, and markets fixed at intake. Out of scope: implementing remediations, performing conformity assessments, and running fundamental-rights impact assessments (FRIAs) — FRIAs route to the AI Governance & Risk/Impact Assessment module.
workflow · Context
Regulatory Horizon Scanning & Triage
Regulatory Horizon Scanning & Triage as a modular, decision-aware workflow. Each cycle runs on an Audit item created for the scan window — `audit_type` = compliance, titled "Regulatory Horizon Scanning — <period>", with `period_start`/`period_end` set to the detection window; the workflow instance attaches to that Audit and is archived against it at close. It can stand alone, but is designed to exchange handoff packages with related workflows instead of duplicating repeated work. In scope: detecting and summarizing regulator publications, routing them to the authority-source register, applicability screening against the compliance profile, entity profile, and activity inventory, owner assignment, and queueing. Out of scope: obligation mapping, gap and impact analysis, and implementation — those belong to the downstream Regulatory Impact Analysis & Obligation Mapping workflow, which consumes this workflow's named deliverables: the prioritized impact-analysis intake queue and the cycle evidence package. There is no upstream workflow and no prior handoff to consume; this is the sensing edge of the regulatory-change chain, and its standing inputs are the monitored feed list, the prior cycle's cursor, the authority-source register, and the Policy items that cite those authorities.
workflow · Context
Regulatory Impact Analysis & Obligation Mapping
Regulatory Impact Analysis & Obligation Mapping runs on a compliance Audit engagement record — an Audit item created per triggering instrument (audit_type=compliance) whose scope holds the locked scope statement and to which every gap Issue, risk acceptance, and the workflow instance attach. It consumes two upstream inputs: the triage handoff package from Regulatory Horizon Scanning & Triage (instrument, canonical citation, publication and effective dates, triage disposition) and the canonical instrument text itself — the Official Journal or regulator-register version, the authoritative source the whole analysis cites. It parses that instrument into obligations, maps them to the existing Policy and Control library, classifies and rates gaps, and reconciles the obligation register. Named deliverables: the cited regulatory impact note, the obligation inventory, the authority-to-policy crosswalk, the classified and rated gap Issues, the versioned obligation register, and the final evidence and decision package. In scope: obligation analysis, crosswalk, gap classification, exposure rating, and register reconciliation for the in-scope entities, products, and jurisdictions. Out of scope: remediation build-out (handed off to Regulatory Obligation Implementation) and re-prioritizing the instrument (owned by Regulatory Horizon Scanning & Triage). Confirmed gaps and their action plans hand off to Regulatory Obligation Implementation.
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Third-Party ICT Vendor Regulatory Assurance
Third-Party ICT Vendor Regulatory Assurance as a decision-aware workflow. Each cycle runs on its own Audit engagement item (audit_type = vendor_review) created at kickoff, with Audit.scope set to the in-scope legal entities, regimes, and vendor population; every workpaper, decision form, and gap attaches to that instance. In scope: ICT third-party service providers — including intra-group ICT providers — assessed against DORA, NIS2, and US interagency/FFIEC third-party expectations; out of scope: non-ICT vendors, which stay with the general vendor lifecycle. It consumes the arrangement-level inventory, tiering, and due-diligence handoff package from Third-Party Vendor Risk Lifecycle (whose provider records are the Vendor items); refers vendor SOC 1/SOC 2 reports carrying real reliance to the Vendor SOC 1/SOC 2 Report Review & CUEC Mapping workflow and folds back its reliance conclusions; and produces a provision-cited, severity-rated gap register (Issue items linked to the anchor Audit and the affected Control/Vendor records) plus a qualified final assurance package. It hands the validated obligation statuses to the Regulatory Compliance Attestation Cycle, exchanging handoff packages with related workflows instead of duplicating repeated work.
workflow · Context
DSAR Fulfillment (Access & Deletion Requests)
DSAR Fulfillment (Access & Deletion Requests) as a modular, decision-aware workflow that verifies identity, locates and reviews personal data, applies exemptions, and delivers the response inside the statutory deadline. The workflow runs against the EXISTING "DSAR / Data-Subject Request Handling" Process item (process_type: business_process) — enrich it, never recreate it: one workflow instance per inbound request, and the instance itself is the DSAR register entry (there is no native DSAR/privacy-request item type; the register is the set of these instances). In scope: a single inbound data-subject access or deletion request under GDPR, CCPA/CPRA, or HIPAA, with the statutory clock started at receipt, carried from identity verification through data compilation, exemption review, delivery, and archival, with any correction or portability elements handled inline. It consumes the inbound request and identity artifacts uploaded at the first step, the data map / record of processing activities, the exemption playbook and retention schedule (Policy items with the governing documents attached), and the processor register (Vendor items, category: data_processing). Named deliverables: the access disclosure set or deletion certificate, the exemption log and redlined package, the reasoned rejection notice on the failed-verification path, the delivery evidence and completion-metrics record, systemic-finding Issue items routed to the privacy program, and the archived DSAR case file — the exported workflow record. Out of scope: unrelated privacy processes such as breach notification and privacy impact assessments, which run as their own workflows; this workflow takes no upstream handoff and produces no downstream workflow handoff.
workflow · Context
Privacy Program Operations (Consent, Complaints & Sharing)
Runs the standing **Privacy Program Operations** process — an existing operational Process item (process_type=operational, process_owner = the privacy officer) — on a recurring cycle: this workflow instance attaches to that Process and IS the cycle record. It enriches what already exists rather than recreating it: the anchor Process, the in-scope RoPA processing-activity Process items, the privacy Control and Policy items, and the still-open Issues carried forward from prior cycles. Three parallel streams run inside the cycle — reconcile consent and preferences against processing activities, resolve privacy complaints with an accounting of disclosures, and govern data-sharing agreements against actual flows — each consuming verifiable extracts (consent-platform events, the disclosure log, integration/transfer logs) and producing named deliverables: the consent reconciliation report, the complaint register (Issue items), executed agreement renewals with any legal-approved interim risk treatments, and the cycle metrics pack, before the cycle is archived to its retention schedule. Self-contained recurring cycle; the approved metrics pack informs downstream privacy governance / committee reporting.
workflow · Context
SOX ITGC Testing
SOX ITGC Testing as a modular, decision-aware workflow. It runs on the existing Audit engagement item for this ITGC cycle (audit_type: sox_testing, period_start/period_end = the test period) — enrich that item, never create a duplicate — while per-control operating-effectiveness results live on Control-hosted SOX testing workflows, one created per in-scope ITGC control per Workflow.customFields.sox.fiscalYear, each hosted directly on the Control under test. It consumes the ICFR scoping handoff package from Annual ICFR Scoping & Risk Assessment, produces the reperformable final ITGC testing package as its named deliverable, and hands the deficiencies off to SOX Deficiency Remediation. In scope: the operating-effectiveness conclusion on the ITGCs protecting in-scope financial systems, reached by referencing the controls-owned NIST 800-53 catalog and its test scripts — not by rebuilding procedures. Out of scope, owned by related workflows: scoping of significant accounts and applications (Annual ICFR Scoping & Risk Assessment), deep completeness-and-accuracy validation of system-generated populations (SOX IPE Validation), and remediation of what fails (SOX Deficiency Remediation, the downstream handoff). It can stand alone, but is designed to exchange handoff packages with these related workflows instead of duplicating repeated work.
workflow · Context
SOX Scoping Decision
Runs on the existing Process item for the one business process under decision — it enriches that Process item and the Control and Risk items in its RCM, never creating a duplicate. Determine whether the process is SOX-relevant and, if so, scope its key controls — otherwise document the exclusion. Consumes the Annual ICFR Scoping & Risk Assessment handoff package (accepted materiality set, scoping thresholds, aggregation floor). Named deliverables: the assessment worksheet, the SOX-relevance determination memo, the resulting Risk & Control Matrix (RCM) scope (Control and Risk items with live links), and the exclusion memo — compiled into a signed scoping decision package. Hands that signed package off to the downstream SOX Process Walkthrough for the in-scope slice, or routes a full exclusion into the annual monitoring/refresh cycle. Out of scope: entity-level materiality, significant accounts, and fraud-risk assessment, which are owned by the upstream Annual ICFR Scoping & Risk Assessment, and process understanding and control verification, which are owned by the downstream SOX Process Walkthrough.