Latest6 min read
AI Orchestrators vs Gateco: Workflow vs Retrieval Control
An orchestrator decides which agents and steps run. Gateco decides what an agent can retrieve. Different layers, and retrieval is where RAG leaks.
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Insights on AI retrieval security, RAG authorization, and data governance.
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7 min readGateco Team
Cerbos vs Gateco: Generic Policy Engine vs Vector-DB-Native
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 →6 min readGateco Team
Why pgvector RLS Breaks Past Ten Tenants
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 →8 min readGateco Team
Azure AI Search vs. Vector Databases for RAG
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 →7 min readGateco Team
Why Metadata Filters Aren't Enough for RAG Security
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 →8 min readGateco Team
Authorization Approaches for RAG Systems: A Comparison
Four approaches to RAG authorization compared: no auth, metadata filters, app-layer RBAC, and a dedicated permission layer. Pros, cons, and when each fits.
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