The RAG Security Gap: Why Semantic Similarity Is Not Authorization

Your organization invested in SSO for identity, IAM for cloud resources, ACLs for file systems, and RBAC for applications. But when you deployed RAG, you created a new access surface that bypasses all of it.

Here's the core problem: when a manager asks your AI copilot about compensation data, the vector database returns the most semantically relevant chunks — not the most appropriately authorized chunks. Semantic similarity does not equal permission to access.

Traditional approaches like metadata filters in vector queries are a partial solution at best. They push authorization logic into application code, can't enforce deny-by-default, and leave no audit trail. What's needed is a dedicated permission-aware retrieval layer.

Gateco sits between your AI agents and vector databases, enforcing RBAC and ABAC policies at retrieval time. Every query is checked against active policies before results are returned. No policy match means no data. Every decision is logged with full context — who requested it, what was allowed or denied, and which policy made the decision.

The result is a retrieval pipeline where your AI agents only see what they're authorized to see, with a complete audit trail for compliance.