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Google has two distinct retrieval products under the Vertex AI brand. Vector Search (formerly Matching Engine) is a managed ANN index. Vertex AI Search is a full Discovery Engine service with keyword, hybrid, and listing capabilities. Gateco supports both. Here is when to use each.

IAM authenticates the agent. Gateco authorizes the data. Why a single IAM role is not enough when your chatbot serves thousands of distinct end users, and the three integration patterns that fix it.

Cerbos is a well-designed generic authorization engine. Gateco is a retrieval-specific security layer built for AI and RAG pipelines. They solve different problems and can be used together. Here is when to choose each.

pgvector Row Level Security is the most common DIY pattern for RAG authorization. Here is when it works, when it breaks, and the five triggers that make teams outgrow it, usually within 6 to 12 months.

Every RAG pipeline your engineering team ships creates a new access surface that bypasses application-layer authorization. Here is how to close the gap, in security language, not developer language.

The most common question about adding an authorization layer to RAG: "How much latency does it add?" Here is exactly how Gateco achieves <25ms p95 policy overhead, what drives variance across connectors, and what happens when the policy engine is slow.

Enterprise AI teams increasingly span multiple clouds. Gateco now enforces the same deny-by-default policies across AWS OpenSearch, Azure AI Search, and Google Vertex AI, so your RAG governance story is consistent regardless of where your vectors live.

Azure AI Search gives you world-class hybrid retrieval. Gateco decides who's allowed to see the results. Here's 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 your governance options, more than most teams realize.

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.

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.

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.

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.

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.

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.