Accountability
Data ownership
Accountable owners, stewards, classification and escalation paths.
Data / Quality / Lineage
Data governance for trustworthy AI
AI assurance depends on reliable data: clear ownership, quality thresholds, provenance, permissions, retention and monitoring.
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Data governance control areas
Quality
Data quality
Accuracy, completeness, timeliness, representativeness and exception handling.
Traceability
Lineage and provenance
Source traceability, transformations, licensing and training or retrieval usage.
Protection
Privacy and access
Purpose limitation, access control, retention and sensitive-data handling.
Suitability
Data readiness
Evaluate whether data is suitable for the proposed AI use case.
Operations
Ongoing monitoring
Detect quality deterioration, source changes and policy violations.