Why Enterprise RAG Systems Need Lineage Governance to Stay Secure

Retrieval-Augmented Generation (RAG) has become a core architecture for enterprise AI agents, pairing large language models with vector databases to handle complex business queries. However, traditional access control frameworks like Role-Based Access Control do not translate into vector embedding spaces, leaving sensitive data exposed to unauthorized retrieval. Key risks include privilege escalation through context injection, indirect prompt injection via malicious documents, and hallucinations caused by stale or outdated embeddings. To address these vulnerabilities, platform engineering teams are urged to implement Data, Context and RAG Lineage Governance, which enforces query-time authorization and cryptographic data provenance. This approach structures RAG pipelines into distinct security boundaries, embedding metadata and access control attributes directly alongside vector representations to maintain a verifiable audit trail.