Insights on AI retrieval security, RAG authorization, and data governance.
Purview classifies, labels, and governs your data estate. It does not decide, at query time, whether a user may retrieve a document from AI you build.
Read full article →An orchestrator decides which agents and steps run. Gateco decides what an agent can retrieve. Different layers, and retrieval is where RAG leaks.
Read more →Cerbos is a generic authorization engine. Gateco is a retrieval-specific security layer for RAG. They solve different problems, and can be used together.
Read more →pgvector Row Level Security is the most common DIY RAG auth pattern. When it works, when it breaks, and the five triggers that make teams outgrow it.
Read more →Azure AI Search is a managed search platform; pgvector, Pinecone, and Qdrant are retrieval primitives. The choice shapes your RAG architecture and governance.
Read more →Metadata filters are the most common approach to RAG access control, and fundamentally insufficient. Why they can't replace a dedicated permission layer.
Read more →Four approaches to RAG authorization compared: no auth, metadata filters, app-layer RBAC, and a dedicated permission layer. Pros, cons, and when each fits.
Read more →Get started with Gateco on the Free tier: 1,000 secured retrievals per month.