Pre-Retrieval vs Post-Retrieval Authorization
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 full article →Insights on AI retrieval security, RAG authorization, and data governance.
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 full article →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 →Gateco is not a RAG framework. It is the authorization layer you insert at the retrieval step of LangChain or LlamaIndex. Here is where it goes, and why.
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 →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 →Google has two retrieval products under the Vertex AI brand: Vector Search, a managed ANN index, and Vertex AI Search, Discovery Engine. When to use each.
Read more →The Gateco MCP server gives Claude Desktop, Cursor, and any MCP host policy-enforced access to your vector knowledge bases. Denied content never surfaces.
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 →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 →How much latency does an authorization layer add to RAG? How Gateco holds under 25ms p95 policy overhead, and what drives variance across connectors.
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 →Gateco now supports four distinct retrieval modes. Here's when to reach for each one, and why hybrid might be your new default.
Read more →DIY RAG authorization needs a policy engine, metadata resolution, audit logging, connector adapters, and identity sync. What it actually takes to build it.
Read more →Get started with Gateco in minutes. Free tier includes 100 secured retrievals per month.