Authorization Approaches for RAG Systems: A Comparison

Every team building RAG hits the same question: how do we control who sees what? The answer depends on your threat model, compliance requirements, and how many AI-consuming applications you're running. Here are the four common approaches, from simplest to most robust.

Approach 1: No authorization. Every query returns the top-k most similar vectors, regardless of who's asking. This works for public knowledge bases and internal tools where all employees have equal access to all data. It stops working the moment you ingest anything with access restrictions — HR data, financial reports, legal documents, customer PII.

Approach 2: Metadata filters. Tag vectors with access metadata (department, classification, sensitivity) and apply filters at query time. Pros: simple to implement, no additional infrastructure. Cons: every application must implement filtering correctly, no deny-by-default (missing tags = unrestricted access), no audit trail, no central policy management. See our deep dive on why metadata filters aren't enough.

Approach 3: Application-layer RBAC. Build authorization logic into your RAG application. Check the user's role before querying, filter results based on role-resource mappings. Pros: familiar pattern, can use existing IAM roles. Cons: authorization logic is coupled to each application, doesn't scale across multiple AI agents, role-resource mappings become unmanageable at scale, still no standardized audit trail across applications.

Approach 4: Dedicated permission layer (Gateco). A middleware between AI agents and vector databases that enforces RBAC and ABAC policies at retrieval time. Pros: central policy definition, deny-by-default, full audit trail, works across all consuming applications, supports attribute-based conditions beyond simple roles, semantic readiness tracking. Cons: additional infrastructure component, requires policy configuration.

The right choice depends on where you are. Prototyping with internal data? No auth is fine. Single application with simple access rules? Metadata filters work. Multiple AI agents accessing sensitive data with compliance requirements? You need a dedicated permission layer. The cost of retrofitting authorization increases exponentially the longer you wait — it's far cheaper to start with the right approach than to bolt it on later.