Entitlement Propagation

The core mechanism that binds a user's authenticated identity to every vector search query. Instead of a generic service account, the retrieval engine executes searches in the security context of the end-user. This ensures that a financial analyst and an intern submitting identical natural language queries receive different, permission-scoped results from the same knowledge base. The user's identity, often passed via a JSON Web Token (JWT) or SAML assertion, is propagated through the retrieval pipeline to enforce document-level, chunk-level, or field-level security.

Entitlement propagation relies on a continuous sync loop with the enterprise identity provider (IdP) or access governance system. Changes made in the source application—such as revoking a user's access to a project folder in SharePoint or updating group membership in Okta—must be reflected in the vector database's access control lists in near real-time. This eliminates the security gap created by stale permissions, where a recently terminated employee could still retrieve sensitive documents from the vector index.

Access rights are materialized as filterable metadata attached to each document chunk. Common patterns include:

• Access Control Lists (ACLs): A list of user or group identifiers permitted to retrieve a chunk.
• Security Labels: Classification tags like 'Confidential' or 'Internal Only' that are matched against user clearance attributes.
• Attribute-Based Conditions: Boolean expressions evaluated at query time, such as user.department == doc.department. The vector database applies these filters as a pre- or post-retrieval step to prune unauthorized results.

A critical design decision is mapping source permissions to the vector index granularity. If a user has access to only a single paragraph in a 50-page document, the chunking strategy must preserve that boundary. Document-level propagation is simpler but risks over-exposure; chunk-level propagation is precise but increases metadata storage and filter complexity. Advanced implementations use hierarchical inheritance, where a chunk inherits permissions from its parent document unless explicitly overridden.

Authorization checks must not introduce significant latency to the retrieval-augmented generation (RAG) pipeline. Entitlement propagation systems often pre-resolve permissions into an optimized index structure—such as a bitmap filter or inverted ACL—to enable sub-millisecond access decisions during vector similarity search. This pre-computation trades off a slight delay in permission update propagation for consistently fast query-time enforcement, maintaining a responsive user experience.

The policy decision point (PDP) remains external to the vector database. The retrieval engine acts as the policy enforcement point (PEP), querying the PDP or its cached decisions. This decoupling allows organizations to maintain a single, auditable authorization policy in a central engine like Open Policy Agent (OPA) or Amazon Verified Permissions, while applying those decisions consistently across source applications and AI retrieval pipelines. It prevents policy fragmentation and ensures compliance with least privilege retrieval principles.