The Learning Enterprise: Governing Reality Based AI Evidence
A practical governance model for enterprise AI evidence, from provenance and claim approval through authorized use, monitoring, correction, and withdrawal.
Data Governance
AI Assisted Internal Investigations: Designing Legal Hold, Privilege, Evidence, and Remediation Boundaries
Design an attorney-controlled investigation workflow from allegation through preservation, evidence review, findings, and remediation. Keep AI inside approved data and authority boundaries, with separate human decisions for privilege, discipline, disclosure, hold release, and closure.
Enterprise Data Analysis as an Evidence System: A Governed Prompt for Defensible Decisions
Build enterprise data analysis around a clear decision, reliable source data, and reproducible methods. This governed prompt defines metrics, validates joins, tests uncertainty, and separates measured results from interpretation.
AI Memory Architecture: Context, RAG, and Persistent State
Choose the right mechanism for AI context, retrieval, persistent records, and workflow state. Design provenance, permitted use, correction, and revocation across both the memory write and read paths.
AI Feedback Governance: Control What Becomes Learning
Control which feedback becomes a persistent change and who may approve it. Use promotion manifests, scoped evaluation, traceable release decisions, and tested withdrawal paths.
A Citation Is Not Proof: Building an Evidence and Disclosure Control Layer
Make RAG citations useful evidence rather than decorative references. Validate source identity, claim support, applicability, and disclosure permission while keeping citation destinations under application control.
Your RAG Pipeline Is a Data Boundary, Not Just a Search Feature
Treat ingestion, chunking, embedding, and storage as governed data processing. Preserve source restrictions and lineage, control processing routes, and design revocation and recovery before expanding the RAG corpus.
Production Vector Database Operations: Backup, Recovery, Reindexing, and Access Control
Recover the retrieval service, not just vector database files. Coordinate metadata, lineage, embeddings, permissions, and configuration, then validate authorized retrieval before reopening production traffic.