Permission-aware AI, also called governed RAG, checks who is asking before it retrieves anything and returns only the knowledge that person or agent is cleared to see, in real time. Unlike plain retrieval-augmented generation, which ranks documents purely by relevance, it enforces each source's permissions per request and can hide sensitive fields within an allowed document.
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 missing question: who is asking, and what are they cleared to see? It resolves the requester's identity, checks their permissions against each source in real time, and retrieves only from the set they are allowed. Relevance still ranks the results, but only within what that person or agent may access.
How it works, step by step
Identity: confirm the person or agent behind the request using your existing login.
Permission check: evaluate that identity against each connected source's real, current access rules.
Scoped retrieval: search and rank only within the documents they are cleared to see.
Field-level redaction: within an allowed document, withhold any sensitive part they should not see.
Record: write the access to a content-blind, tamper-evident log.
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.