Insights on AI retrieval security, RAG authorization, and data governance.
Metadata filters are the most common approach to RAG access control. They're also fundamentally insufficient. Here's why app-level filtering can't replace a dedicated permission layer.
Read full article →Four approaches to RAG authorization, compared: no auth, metadata filters, app-layer RBAC, and a dedicated permission layer. Pros, cons, and when each makes sense.
Read more →DIY RAG authorization requires a policy engine, metadata resolution, audit logging, connector adapters, and identity sync. Here's what it actually takes to build it yourself.
Read more →Vector databases retrieve based on embedding similarity. They don't know who's asking. They don't check permissions. They just return the closest matches. This is the AI security gap — and it's wider than most teams realize.
Read more →Gateco assigns each connector a readiness level from L0 to L4 based on its security capability — not a percentage, but a progression through increasingly granular enforcement. Here's what each level means and how to reach it.
Read more →SaaS platforms embedding LLM features must prevent cross-tenant data leakage in shared RAG infrastructure. Here's how to enforce tenant isolation at the retrieval layer.
Read more →Get started with Gateco in minutes. Free tier includes 100 secured retrievals per month.