How do you enforce access control in a RAG pipeline?

Filter at retrieval time, against the identity of the requesting user, before any chunk reaches the model. Once text is in the context window it has already leaked — filtering the model's output afterwards is damage limitation, not access control. The retriever is the enforcement point.

The failure mode is structural. Enterprise sources — document stores, wikis, ticketing systems — already carry identity-based permissions, but a naive RAG pipeline indexes their contents into a vector store that has none. Every user then queries a corpus flattened to the union of everyone's access.

The fix is to carry the source permissions into the index and apply them as a pre-retrieval filter bound to the caller's identity. That is the architecture we built for a US AI-security company: connectors keep a live inventory of what the AI can reach, and a wrapper around every retrieval applies the caller’s permissions before any text is returned.

Audit the filter, not just the answer. You want a log showing which chunks were withheld for which principal, because that is the artefact a compliance review asks for.

Written by Binary AI Labs · Reviewed 2026-11-01