RBAC assigns permissions to roles, finance analyst, HR manager, external contractor, rather than to individual people, and grants access based on which role a user holds. On a traditional document store, that permission lives on the folder or file. On an AI system built to retrieve across unified company data, it has to live somewhere else entirely: on the query itself.
That distinction is what makes RBAC a design requirement for enterprise AI, not just an IT policy. A retrieval system that doesn’t check the requester’s role before returning results will happily surface a document above their clearance, because nothing in a search index inherently knows who’s asking. Enforcing RBAC at query time, filtering candidate results against the user’s role before anything reaches the model, is what keeps a unified data platform from becoming a company-wide search engine for restricted files.
It only closes one of the two leakage paths, though. RBAC controls what a retrieval-augmented system can serve; it does nothing for facts a model has already memorised through fine-tuning, which is why the two need separate, deliberate fixes.