Permission-Aware Retrieval: ACLs and Row-Level Security
Explore the principles of permission-aware retrieval that enforce authorization at the data retrieval stage in AI systems. Understand how access control lists and row-level security policies prevent unauthorized data exposure in multi-tenant environments. Learn to define explicit policies, handle entitlement evaluation and failures, and secure data retrieval to support enterprise governance and security requirements.
Context construction rules assume the candidates arriving at that stage are already ones the caller is allowed to see. That assumption has to be enforced somewhere. It can’t be enforced after the fact. Retrieval must enforce authorization, not just the user interface. The retrieval stage decides what data reaches context construction, and therefore what the model can see at all. Permission-aware retrieval is the control that prevents cross-boundary data leakage when users belong to shifting groups. Access here is a mix of document policies and row-level access to customer and account records.
A permission-aware retrieval contract places an explicit policy decision point in the request path before any candidate documents or rows get assembled into prompts, cached, logged, or cited. Filtering after retrieval is not enough. By then, the system has already handled sensitive content in intermediate steps: ranking features, trace logs, evaluation datasets, citation snippets. Those steps routinely cross different trust boundaries than the final answer surface does.
Multi-tenancy sharpens this further, since the same retrieval infrastructure can serve multiple tenants while the model and ...