AI Memory Governance

The compliance, audit, lineage, and retention discipline applied to persistent AI memory — extending traditional data governance to cover the case where data has been absorbed into vector embeddings, agent memory graphs, or model weights rather than living as cleanly deletable objects.

Standard vector databases treat all ingested context equally, lacking the structural nuance enterprise compliance requires. GDPR Article 22 ("Right to be Forgotten") becomes hard when a user's data has been embedded into vectors or absorbed into continuous-learning weights — simply deleting the source object is insufficient. AI Memory Governance names the architectural responses: **Constitutional Memory Architecture** (immutable rule layers), **Forgetting-as-a-Service** (gradient-based unlearning + cryptographic shredding), retrieval auditability (cryptographic provenance chains from response back to source object), and memory lineage tracking.

Retrieval audit chains tying generated text to specific S3 object versions, memory-lineage cascading invalidation when source objects change, AGI/agentic compliance for regulated industries, deletion-semantics tagging on persistent memory tiers, semantic claim graphs binding generated facts to source documents.