How to Enforce Document-Level Permissions in Enterprise RAG

Every system you ingest from already knows who is allowed to read each document. A file store has its access control lists, your database has row ownership, your wiki has space membership. The moment you embed those documents into a vector store, that knowledge is gone: an embedding is a list of numbers with no idea who owned the source. This is the end-to-end recipe for carrying document-level permissions through a RAG pipeline, so the chunk a user retrieves respects the same access rules as the document it came from.

Step 1: Sync identity, so every query has a principal

Authorization needs a subject. Connect your identity provider, Okta, Microsoft Entra ID, AWS IAM Identity Center, or GCP Cloud Identity, and Gateco syncs users into principals with their groups and attributes such as department. From then on, every retrieval resolves to a named principal before any policy runs. If your AI agent currently queries with a single shared service account, this is the first thing to fix: anonymous retrieval cannot be authorized.

Step 2: Classify what you ingested

Policies decide against metadata, so each resource needs a classification: public, internal, confidential, or restricted, or whatever scheme you use. Two features make this tractable on a real corpus. Classification suggestions scan your resources with rule-based patterns and propose labels for an admin to review, so you are correcting a draft rather than labeling from scratch. Retroactive registration scans a connector for vectors that were ingested before Gateco was in place and registers them as managed resources, so nothing sits unmanaged and therefore ungoverned.

Step 3: Bind the metadata to the vectors

Gateco resolves a resource's metadata through one of three modes. Sidecar, the default, keeps metadata on Gateco's own resource records and is always available after ingestion. Inline reads metadata straight from the vector payload, for stores where each chunk already carries its own labels. SQL view reads from a structured view, for the Postgres family. Most teams start with sidecar because it asks nothing special of the vector store, and move to inline or SQL view only when they need per-chunk granularity.

Step 4: Write the policy

With principals and classifications in place, the policy is short. A classification ceiling is the workhorse: a principal cannot receive a chunk classified above their clearance. Add group rules for team-scoped access and a deny rule for the most sensitive material. Gateco ships templates for each of these, so you are filling in placeholders rather than authoring from a blank page. One rule that catches everyone at least once: condition fields need a resource. or principal. prefix. resource.classification checks the document; a bare classification silently resolves against the principal instead, and quietly does nothing useful.

Step 5: Enforce and prove it at retrieval

Now every retrieval is checked against the active policies before any chunk is returned, deny-by-default, and each decision is written to the audit log with the principal, the resource, the policy that decided, the outcome, the search mode, and a timestamp. That log is the evidence your security or compliance team asks for, and it exports to CSV or JSON. The permission your source system enforced is now enforced again, at the point where the AI actually reads.

Where this tends to break

Three failure modes account for most leaks. Bare condition fields that resolve against the principal instead of the resource, covered above. Untagged resources under a filtering scheme that fails open, which is why deny-by-default plus a sidecar default beats a query-side filter. And authorization logic scattered across every consuming application, where the second app to query the store implements the check slightly differently, or not at all. Enforcing once, at the retrieval layer, removes all three.

For the deeper argument about why this belongs at retrieval time rather than in the query or in your app code, see pre-retrieval vs post-retrieval authorization. The enforcement model and audit spec are on the security page.