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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.

An orchestrator decides which agents and steps run. Gateco decides what an agent can retrieve. Different layers, and retrieval is where RAG leaks.

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.

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.

IAM authenticates the agent. Gateco authorizes the data. Why one IAM role is not enough when your chatbot serves thousands of users, and how to fix it.

Cerbos is a generic authorization engine. Gateco is a retrieval-specific security layer for RAG. They solve different problems, and can be used together.

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.

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.

How much latency does an authorization layer add to RAG? The measured answer, with a public benchmark: 16ms p50 and 21ms p95 policy overhead, and what drives variance across connectors.

Gateco enforces the same deny-by-default policies across AWS OpenSearch, Azure AI Search, and Google Vertex AI, so RAG governance stays consistent everywhere.

Azure AI Search gives you hybrid retrieval. Gateco decides who can see the results. Why enterprise RAG needs both, and how they compose.

Azure AI Search is a managed search platform; pgvector, Pinecone, and Qdrant are retrieval primitives. The choice shapes your RAG architecture and governance.

Metadata filters are the most common approach to RAG access control, and fundamentally insufficient. Why they can't replace a dedicated permission layer.

Four approaches to RAG authorization compared: no auth, metadata filters, app-layer RBAC, and a dedicated permission layer. Pros, cons, and when each fits.

DIY RAG authorization needs a policy engine, metadata resolution, audit logging, connector adapters, and identity sync. What it actually takes to build it.

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.

Gateco assigns each connector a readiness level from L0 to L4, based on security capability rather than a percentage. Here is what each level means.

SaaS platforms with LLM features must prevent cross-tenant leakage in shared RAG infrastructure. How to enforce tenant isolation at the retrieval layer.