BasicAgent
AI Data Governance
AI data governance that covers training data lineage, access controls, and retention for ML and LLM systems.
AI data governance defines how data is collected, stored, used, and audited across AI workflows.
Key controls
- data inventory and lineage tracking
- access and usage policies
- retention and deletion rules
- redaction and privacy safeguards
Evidence to keep
- data source approvals
- dataset versioning and change logs
- evaluation reports tied to data changes
Related pages
- AI governance framework:
/ai-governance-framework/ - AI model tracking:
/ai-model-tracking/ - RAG provenance:
/rag-provenance-citations/
Create account
Build narrative
Follow a coherent path from thesis to lab notes to proof-of-work instead of isolated pages.
Step 1
Intelligence systems office
The strategic map for what is being built and why.
Step 2
Lab notes
Build footprints and progression logs as proof-of-work.
Step 3
Control surface
Governance and monitoring architecture for operational reliability.
Step 4
Private alignment
Convert insight into execution with scoped collaboration.