Practical guide
RAG access control
Access control for retrieval means one thing in practice: when two people ask your AI the same question, each gets only what they are cleared to see. This page is the working path to that state on the vector database and identity provider you already run.
Govern the data you already have
Connect the vector database, register existing vectors retroactively, and attach classification metadata in bulk with admin-reviewed suggestions. Ingest new documents through a pipeline that registers every chunk.
Bring in real identities
Sync principals, groups, and attributes from Okta, Azure Entra ID, AWS IAM Identity Center, or GCP Cloud Identity, or provision over SCIM. Policies about people require the actual people, not hardcoded role strings.
Write policies against metadata, not documents
RBAC for group access, ABAC for classification ceilings and department scoping, relationship-based rules for imported source permissions. Deny-by-default means an unclassified chunk stays invisible rather than leaking.
Simulate, enforce, audit
Preview what a specific person would retrieve before policies go live. Then let every real retrieval produce an audit record: who asked, what was allowed, what was denied, and which policy decided.
When you do not need any of this
If every user of the system may see every document in the index, add nothing: an authorization layer with one policy that allows everything is overhead with no benefit. A stable two-tier corpus behind a single trusted service is often fine with the vector database's own metadata filter. The threshold is crossed when permissions come from an identity provider, differ per user, change often, or must be provable to an auditor. The trade-offs are covered honestly in why metadata filters are not enough and build vs buy.
Setup guides for your stack
From zero to enforced in nine steps
The getting-started guide walks the whole path above, with a 3-minute video version.