Data Governance for AI Workloads

AI workloads violate nearly every assumption that traditional data governance was built on. The policies that work for SQL queries and BI dashboards need rethinking.

How AI Breaks Traditional Governance

Traditional model: a user queries specific columns from specific tables. Access control, audit logging, and data lineage are well-understood.

AI model: a user asks a natural language question. The system retrieves documents from multiple sources, feeds them to an LLM, and synthesizes a response. Which data was accessed? Which parts influenced the answer? The audit trail is fuzzy.

The Four Controls You Actually Need

1. Input logging: log every document chunk that entered the context window, with timestamps and user identity. This is non-negotiable for regulated industries.

2. Access filtering at query time: as discussed in our knowledge base architecture post, access control must happen at the vector retrieval layer, not in post-processing.

3. Output logging with retention policies: all LLM outputs should be logged and retained per your data retention schedule. This is both an audit requirement and a quality baseline.

4. PII detection before ingestion: scan all documents for PII before indexing. Embedding PII into a vector store creates a retrieval surface that is hard to audit and harder to clean up.

Regulatory Considerations

For financial services, the key question from regulators is: "Can you explain why the system gave this advice?" This requires not just output logging but retrieval logging — the specific documents that informed each response.

For healthcare, HIPAA applies to any PHI that enters the AI pipeline. Anonymization before ingestion is mandatory, not optional.