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 specific user may retrieve a specific document from the AI your team is building. That gap has a name: retrieval authorization.
Read full article →A RAG pipeline can enforce access inside the vector query or in the app after results return. Each has a distinct failure mode. Here is what breaks.
Read more →Embeddings throw away the permissions your source systems already track. Here is the recipe to carry document-level permissions into a RAG pipeline.
Read more →Gateco supports role, attribute, and relationship-based access control, and you can mix them in one policy set. Here is which model fits which pattern.
Read more →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 →Gateco now supports per-org OpenAI keys for Grounded Answers, encrypted with AES-256-GCM and per-tenant KMS binding. Here is how the credit model works.
Read more →When policy evaluation hits an error, Gateco denies the retrieval and logs it. Here is why fail-closed is the right default, and when fail-open fits.
Read more →Gateco now supports 1-hop relationship-based access control: policies can check whether a principal owns or is assigned to a resource. How and when to use it.
Read more →Every RAG pipeline your team ships creates an access surface that bypasses application-layer authorization. Here is how to close the gap, in security terms.
Read more →A summary of everything that shipped this month: relationship-based access control, API key authentication, SDK v1.0, and our new Trust Center.
Read more →Azure AI Search gives you hybrid retrieval. Gateco decides who can see the results. Why enterprise RAG needs both, and how they compose.
Read more →Azure AI Search has powerful retrieval, but for compliance it leaves three gaps: no dynamic ABAC, no deny-by-default, and no audit trail.
Read more →A step-by-step guide to connecting your identity provider to Gateco for policy-enforced AI retrievals.
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 →Vector databases retrieve by embedding similarity. They don't know who's asking or check permissions. That is the RAG security gap, and it is wide.
Read more →Get started with Gateco in minutes. Free tier includes 1,000 secured retrievals per month.