Phase 3: Enterprise RAG Systems · 50 min · Python · Pinecone · PostgreSQL

Document-Level Access Control

Not all documents are for all users. Enforce it at retrieval time.

What you will learn

Implement row-level security in vector stores: filtering retrieved chunks by user permissions
Tag chunks with access control metadata: department, clearance level, project
Design tenant isolation: per-tenant indexes vs. filtered shared indexes
Evaluate vector DB support for user-level filtering as a hard selection requirement
Implement audit logging: tracking which documents were retrieved for which user

Objective

What you'll learn

This lesson takes approximately 50 min. By the end, you will be able to:

Implement row-level security in vector stores: filtering retrieved chunks by user permissions
Tag chunks with access control metadata: department, clearance level, project
Design tenant isolation: per-tenant indexes vs. filtered shared indexes
Evaluate vector DB support for user-level filtering as a hard selection requirement
Implement audit logging: tracking which documents were retrieved for which user

The Problem

In an enterprise, not all documents are for all users. The CEO's board memo shouldn't appear in a junior employee's search results. Document-level access control enforces permissions at retrieval time — not at the UI level, but in the vector database query itself.

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Key terms in this lesson

RAG (Retrieval-Augmented Generation)