Data governance sets the rules for responsible, secure use of enterprise data in generative AI applications. Policies should address ownership, privacy, and security, including how generated data is handled. Keep responsible-AI policies alongside them for risks such as bias, harmful content, and hallucinations. section 1.7
Apply those rules throughout the application workflow:
- Choose a focused pilot and a clear use case before scaling; its data needs help determine the required controls. section 1.7
- For retrieval-augmented generation (RAG), curate and regularly update the document index. Outdated or irrelevant records weaken responses, while confidential records require privacy and security protections. section 7.6
- Use orchestration to help enforce rules for how data is processed, stored, and used across a workflow; check that those rules meet enterprise policy and privacy requirements. section 10.3.1
- Carry governance into LLMOps through monitoring, traceability, security, and feedback as applications change in production. section 11.6
A confidential-document RAG application
Before indexing enterprise documents, identify which information is confidential and what privacy protections it needs. Maintain the index as documents change, then check that workflow processing and storage follow the applicable policies. A technically working retrieval system can still return poor answers from stale records or mishandle sensitive data. section 7.6 section 10.3.1
