unified
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.
Record JSON · Open in map · Data retrieval guide
Catalog revision: 24028ffcfc2b295fa1b08ee6caa84b765f0731b321496bf4f548c49ad2177028. A connection does not establish full coverage.
Attributes
- domain
- AI Governance
- type
- preventive
- category
- technical
Details
- unified_id
- UC-AI-09
- title
- Govern AI data quality, provenance, and preparation
- statement
- 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.
- domain
- AI Governance
- control_type
- preventive
- control_category
- technical
- members
- framework
- iso-42001
- control_id
- A.7.2
- coverage
- full
- relationship
- superset_of
- framework
- iso-42001
- control_id
- A.7.4
- coverage
- full
- relationship
- superset_of
- framework
- iso-42001
- control_id
- A.7.5
- coverage
- full
- relationship
- superset_of
- framework
- iso-42001
- control_id
- A.7.6
- coverage
- full
- relationship
- superset_of
- framework
- eu-ai-act
- control_id
- AIA-Art10
- coverage
- full
- relationship
- superset_of
- framework
- iso-42001
- control_id
- A.7.3
- coverage
- full
- relationship
- superset_of
- guidance
- source
- nist-ai-agent-identity
- sourceTitle
- NIST NCCoE: Software and AI Agent Identity and Authorization
- propositionId
- NIST-AGI-07
- propositionTitle
- Prompt provenance and data-flow tracking
- sourcePages
- Concept paper p. 6: Tracking Data Flows of an AI System
Source
No record-specific source URL is provided.
Connections
- UC-AI-09 — Govern AI data quality, provenance, and preparation mitigates Poor-quality, unrepresentative or mislabeled training data
- strength
- primary
- rationale
- Quality criteria (representativeness, completeness, error-freedom) plus cleaning/labelling governance directly prevents small, unrepresentative or mislabeled training data.
- UC-AI-09 — Govern AI data quality, provenance, and preparation mitigates AI privacy leakage and re-identification
- strength
- related
- rationale
- Governing lawful data acquisition and provenance addresses training on data without consent or legal basis.
- UC-AI-09 — Govern AI data quality, provenance, and preparation maps_to A.7.4 — Data quality for AI systems
- framework
- iso-42001
- control_id
- A.7.4
- coverage
- full
- relationship
- superset_of
- delta
- Not provided
- source_version
- 2023
- provenance
- mapper
- coworkcanvas-compliance-graph
- reviewDate
- 2026-09-07
- direction
- canonical_to_source
- defaultConfidence
- medium
- defaultStatus
- active
- note
- Each member is a documented relationship claim from the canonical unified control to a source control or guidance proposition. relationship: equal|superset_of (full) / intersects_with|subset_of (partial) / informs (guidance). confidence 'medium' = single-mapper, documented, not yet externally corroborated. source_version is the member framework's edition from the standard version register.
- UC-AI-09 — Govern AI data quality, provenance, and preparation mitigates Harmful AI bias and discrimination against protected groups
- strength
- primary
- rationale
- Examining datasets for biases and mitigating those affecting rights is the data-source control against discriminatory model behavior.
- UC-AI-09 — Govern AI data quality, provenance, and preparation maps_to A.7.6 — Data preparation
- framework
- iso-42001
- control_id
- A.7.6
- coverage
- full
- relationship
- superset_of
- delta
- Not provided
- source_version
- 2023
- provenance
- mapper
- coworkcanvas-compliance-graph
- reviewDate
- 2026-09-07
- direction
- canonical_to_source
- defaultConfidence
- medium
- defaultStatus
- active
- note
- Each member is a documented relationship claim from the canonical unified control to a source control or guidance proposition. relationship: equal|superset_of (full) / intersects_with|subset_of (partial) / informs (guidance). confidence 'medium' = single-mapper, documented, not yet externally corroborated. source_version is the member framework's edition from the standard version register.
- UC-AI-09 — Govern AI data quality, provenance, and preparation maps_to A.7.3 — Acquisition of data
- framework
- iso-42001
- control_id
- A.7.3
- coverage
- full
- relationship
- superset_of
- delta
- Not provided
- source_version
- 2023
- provenance
- mapper
- coworkcanvas-compliance-graph
- reviewDate
- 2026-09-07
- direction
- canonical_to_source
- defaultConfidence
- medium
- defaultStatus
- active
- note
- Each member is a documented relationship claim from the canonical unified control to a source control or guidance proposition. relationship: equal|superset_of (full) / intersects_with|subset_of (partial) / informs (guidance). confidence 'medium' = single-mapper, documented, not yet externally corroborated. source_version is the member framework's edition from the standard version register.
- UC-AI-09 — Govern AI data quality, provenance, and preparation maps_to A.7.5 — Data provenance
- framework
- iso-42001
- control_id
- A.7.5
- coverage
- full
- relationship
- superset_of
- delta
- Not provided
- source_version
- 2023
- provenance
- mapper
- coworkcanvas-compliance-graph
- reviewDate
- 2026-09-07
- direction
- canonical_to_source
- defaultConfidence
- medium
- defaultStatus
- active
- note
- Each member is a documented relationship claim from the canonical unified control to a source control or guidance proposition. relationship: equal|superset_of (full) / intersects_with|subset_of (partial) / informs (guidance). confidence 'medium' = single-mapper, documented, not yet externally corroborated. source_version is the member framework's edition from the standard version register.
- UC-AI-09 — Govern AI data quality, provenance, and preparation maps_to AIA-Art10 — Data and data governance (high-risk)
- framework
- eu-ai-act
- control_id
- AIA-Art10
- coverage
- full
- relationship
- superset_of
- delta
- Not provided
- source_version
- Regulation (EU) 2024/1689
- provenance
- mapper
- coworkcanvas-compliance-graph
- reviewDate
- 2026-09-07
- direction
- canonical_to_source
- defaultConfidence
- medium
- defaultStatus
- active
- note
- Each member is a documented relationship claim from the canonical unified control to a source control or guidance proposition. relationship: equal|superset_of (full) / intersects_with|subset_of (partial) / informs (guidance). confidence 'medium' = single-mapper, documented, not yet externally corroborated. source_version is the member framework's edition from the standard version register.
- ISO/IEC 42001 AI Management System Internal Audit tests UC-AI-09 — Govern AI data quality, provenance, and preparation
- UC-AI-09 — Govern AI data quality, provenance, and preparation mitigates Inaccurate, unreliable or hallucinated AI outputs
- strength
- related
- rationale
- Higher-quality, representative training data reduces garbage-in errors upstream of output accuracy.
- UC-AI-09 — Govern AI data quality, provenance, and preparation maps_to A.7.2 — Data for development and enhancement of AI systems
- framework
- iso-42001
- control_id
- A.7.2
- coverage
- full
- relationship
- superset_of
- delta
- Not provided
- source_version
- 2023
- provenance
- mapper
- coworkcanvas-compliance-graph
- reviewDate
- 2026-09-07
- direction
- canonical_to_source
- defaultConfidence
- medium
- defaultStatus
- active
- note
- Each member is a documented relationship claim from the canonical unified control to a source control or guidance proposition. relationship: equal|superset_of (full) / intersects_with|subset_of (partial) / informs (guidance). confidence 'medium' = single-mapper, documented, not yet externally corroborated. source_version is the member framework's edition from the standard version register.
- UC-AI-09 — Govern AI data quality, provenance, and preparation mitigates Unfair exclusion by AI in education and training
- strength
- primary
- rationale
- Data preparation/quality and bias examination address the training-data bias central to education-AI fairness requirements.
- UC-AI-09 — Govern AI data quality, provenance, and preparation mitigates Adversarial attacks, data poisoning and prompt injection
- strength
- related
- rationale
- Provenance recording and data-selection governance reduce training-data poisoning and embedded backdoors.
- AI System Development, Data & Deployment Gate operates UC-AI-09 — Govern AI data quality, provenance, and preparation
- AI Governance & Risk/Impact Assessment oversees UC-AI-09 — Govern AI data quality, provenance, and preparation
- UC-AI-09 — Govern AI data quality, provenance, and preparation mitigates Lack of AI explainability, documentation and disclosure
- strength
- related
- rationale
- Provenance/lineage records supply the datasheets the risk says are missing, though indirect to core model opacity.
- UC-AI-09 — Govern AI data quality, provenance, and preparation mitigates Privacy harms: distortion, stigmatization, unwarranted restriction
- strength
- related
- rationale
- Data accuracy/relevance criteria reduce processing of inaccurate or out-of-context data (distortion harm).
- UC-AI-09 — Govern AI data quality, provenance, and preparation informed_by NIST-AGI-07 — Prompt provenance and data-flow tracking
- framework
- nist-ai-agent-identity
- control_id
- NIST-AGI-07
- coverage
- guidance
- relationship
- informs
- delta
- Not provided
- source_version
- February 2026 draft concept paper
- provenance
- mapper
- coworkcanvas-compliance-graph
- reviewDate
- 2026-09-07
- direction
- canonical_to_source
- defaultConfidence
- medium
- defaultStatus
- active
- note
- Each member is a documented relationship claim from the canonical unified control to a source control or guidance proposition. relationship: equal|superset_of (full) / intersects_with|subset_of (partial) / informs (guidance). confidence 'medium' = single-mapper, documented, not yet externally corroborated. source_version is the member framework's edition from the standard version register.
- sourcePages
- Concept paper p. 6: Tracking Data Flows of an AI System