Most RAG tutorials focus on relevance — chunking strategies, embedding models, and hybrid search fusion. What they rarely address is security. In production, retrieval pipelines pull data from sources with different access levels, sensitivity classifications, and regulatory requirements. A support agent should not see executive compensation data just because the vector similarity score is high. An AI agent processing customer queries should not return internal audit findings because they share vocabulary with the question.This article fills that gap. It walks through adding identity-aware access controls to an existing RAG pipeline as a thin security layer: per-chunk permission enforcement before retrieval, prompt injection scanning, secure context construction for the language model, and audit logging for compliance. Each step produces a concrete artifact you can adapt. All examples use plain Python.