RAG access control: 3 ways to enforce document permissions in AI apps

Permission-aware AI systems must determine a user’s authorized data before answering, and the appropriate enforcement model depends on where content and policy decisions reside. Databricks Apps uses on-behalf-of-user authorization, forwarding a short-lived user token so Unity Catalog applies current row filters and column masks directly, while AWS’s approach for federated SaaS content combines cached ACL filtering with live source-system checks for retrieved candidates to reduce stale-permission risks. For large, owned RAG corpora, a third approach authorizes stable parent resources such as collections or workspaces and filters vectors locally by parent metadata, avoiding per-chunk authorization records. Across all models, systems should fail closed when user tokens, live checks, or authorization queries fail, because silently falling back to an application service identity can expose data under broader permissions. Production applications may use multiple models or identities for different request paths, but user-specific queries must be clearly separated from app-owned operations and executed with the intended authorization context.

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