Most RAG tutorials focus on relevance — chunking strategies, embedding models, and hybrid search fusion. A support agent should not see executive compensation data just because the vector similarity score is high.
Most RAG tutorials focus on relevance — chunking strategies, embedding models, and hybrid search fusion.
A support agent should not see executive compensation data just because the vector similarity score is high.
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...
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Most RAG tutorials focus on relevance — chunking strategies, embedding models, and hybrid search fusion. A support agent should not see executive compensation data just because the vector similarity score is high. 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.
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.
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