The definitive guide
RAG authorization
Vector search ranks by similarity, not by permission. The moment an organization embeds documents with different audiences into one index, its retrieval pipeline can hand any user content they were never allowed to see. RAG authorization is the discipline of re-establishing access control at retrieval time. This page defines the problem precisely, lays out every architectural option honestly, and links the deep-dive material.
The exact failure
A retrieval pipeline embeds documents into a vector database and answers questions by fetching the most similar chunks. Three facts combine into a security failure:
- Embedding strips permissions. Source ACLs (SharePoint, Drive, database grants) do not travel with the vectors.
- The service account sees everything. The RAG backend queries the vector database with one credential that can read the whole index. IAM authorized the application, not the person asking.
- Similarity is not authorization. The database returns whatever is semantically closest. A question about compensation retrieves the compensation documents for whoever asks.
The failure occurs at query time, on every query, silently. It surfaces when an intern asks the internal copilot about salary bands, when a contractor asks about runway, or when an auditor asks for proof of what the AI retrieved and no record exists. It is encountered by any team operating RAG over content with more than one audience: platform engineers who built the pipeline, security teams who inherit it, and compliance owners who must attest to it.
The architectural options, honestly
Four families of solutions exist. Each is the right answer for someone; three of them do not involve Gateco.
Vector DB metadata filtering
Strengths: Zero extra infrastructure, lowest latency, available in every major vector database today.
Limits: Filters are assembled by the calling application, so a bug or prompt-injected query can skip them. No identity model, no audit trail, no deny-by-default.
Best fit: Single-tenant corpora with coarse, stable access tiers.
Pinecone RBAC comparison →Policy engines (Cerbos, Oso, OPA)
Strengths: Mature, well-documented policy languages. Excellent for API and infrastructure authorization across the whole stack, far beyond RAG.
Limits: They evaluate decisions; they do not retrieve. You build and own the integration that fetches candidates, resolves identity, evaluates every chunk, and logs the outcome.
Best fit: Teams standardizing all application authorization on one engine, with engineering capacity to own the retrieval integration.
Cerbos comparison →Data governance suites (Microsoft Purview)
Strengths: Deep Microsoft 365 integration, sensitivity labels, DLP, and compliance reporting in one place. The right tool for M365 Copilot surfaces.
Limits: Enforcement is strongest inside the Microsoft ecosystem. Custom RAG pipelines on Pinecone, pgvector, or Qdrant sit outside its enforcement path.
Best fit: Organizations whose AI surface is primarily Microsoft Copilot over SharePoint and OneDrive.
Purview comparison →Permission-aware retrieval layer (Gateco)
Strengths: Purpose-built for the retrieval path: identity sync from your IDP, per-chunk policy evaluation, deny-by-default, audit trail, and the same enforcement across 12 vector databases.
Limits: Another component in the request path. It governs retrieval only: it does not do prompt-injection defense, model security, or DLP on non-retrieval channels.
Best fit: Different users must get different results from the same corpus, across one or more vector databases, with auditability requirements.
Gateco architecture →Where Gateco fits, and where it does not
Gateco is appropriate when
- A RAG chatbot, copilot, or agent serves users with different permissions from one index
- Documents synced from SharePoint, Drive, or Confluence must keep their source permissions
- Security or compliance requires an audit trail of what the AI retrieved, for whom, and why
- Multiple vector databases need one consistent enforcement and policy model
- You need access revocation to take effect on the next query, without re-indexing
Gateco is not appropriate when
- Everyone in the system is allowed to see everything in the corpus
- You need prompt-injection defense, model red-teaming, or output moderation (different problem, different tools)
- Your only AI surface is Microsoft 365 Copilot (Purview covers it natively)
- You need general API or infrastructure authorization (use OPA, Cerbos, or Oso directly)
- Sub-millisecond retrieval budgets where an extra network hop is unacceptable
Verifiable evidence for the product claims above: the architecture reference, the 9-step integration guide, the recorded setup walkthrough, and the Azure Marketplace listing.
Go deeper
The RAG Authorization Gap for Security Leaders
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.
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.
The RAG Security Gap: Similarity Is Not Authorization
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.
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.
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.
Building Custom RAG Authorization vs. Using Gateco
DIY RAG authorization needs a policy engine, metadata resolution, audit logging, connector adapters, and identity sync. What it actually takes to build it.
Compare every approach
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