Permission-aware AI, also called governed RAG, adds identity and access checks to retrieval instead of ranking material by relevance alone. Its effective boundary depends on the connector, workspace, direct-read paths and caches. Verify those paths and permission-change behavior; field-level restrictions and access-record coverage are separate capabilities to establish for the chosen system.
What plain RAG gets wrong
Retrieval-augmented generation, or RAG, makes AI answer from your documents by finding the most relevant passages and feeding them to the model. The problem: relevance does not care who is asking. If a sensitive document is the most relevant match, plain RAG retrieves it, regardless of whether the person should see it. Build that on a shared index and you have rebuilt the oversharing problem inside your AI.
What permission-aware means
Permission-aware retrieval adds the question: who is asking, and which resources may this retrieval path return? Establish the effective identity and resource boundary, then test that relevance ranking stays within it. Source changes, manual copies and caches need explicit propagation and revocation checks.
How it works, step by step
Identity: confirm the person or agent behind the request using your existing login.
Permission check: evaluate the requester against the supported access rules and verify their source and freshness.
Scoped retrieval: search and rank only within the documents they are cleared to see.
Field-level restrictions: verify that the chosen system can enforce the required boundary; otherwise exclude the mixed-sensitivity document or supply an approved version.
Record: retain the available authorization and access evidence, with its coverage, retention and sensitivity limits.
Why field-level redaction matters
Access is rarely all-or-nothing. A salesperson may read a customer account but not its contract value; an employee may read a team doc but not the compensation column. Permission-aware AI that can redact a single field, rather than refusing the whole document, keeps AI genuinely useful while keeping the secret parts secret.
Common pitfalls
Copying everything into one vector index and applying permissions after the fact, which is fragile and easy to bypass. Caching results across users, which leaks one person's retrieval to another. And forgetting agents: an autonomous agent needs the same permission checks, guardrails, and audit as a person.