AI Data Governance

Audit logs must capture vector retrieval and authorization chains to survive compliance review.

Agents need permissions checked per query, not once at session start.

Four governance layers help AI teams pick the right open source tools.

AI-driven systems fail due to data governance gaps, not flawed models.

Dynamic masking evaluates sensitivity at query time, not copy time.

Autonomous agents need lineage tracking built for continuous chains, not one-off queries.

Data governance built for humans fails when agents query continuously at scale.

Most AI projects fail because governance happens at the wrong layer in the stack.

Stewardship roles collapse when ML pipelines cross domains simultaneously.