Authorization Before Retrieval: Making RAG Safe by Construction
The article discusses the importance of implementing authorization in retrieval-augmented generation (RAG) systems to ensure data security. It emphasizes that while RAG enhances the usefulness of language models by grounding them in real data, it also raises concerns about who can access what information. The author proposes that authorization should be integrated into the retrieval process itself, rather than relying solely on prompts to restrict access to sensitive data.
- ▪Retrieval-augmented generation (RAG) allows language models to utilize real data for improved responses.
- ▪Authorization must be enforced before retrieval to prevent unauthorized access to sensitive information.
- ▪The article argues that relevance in data retrieval does not equate to authorization, highlighting the need for a robust authorization framework.
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Authorization Before Retrieval: Making RAG Safe by Construction Phil Windley // Wed Jan 7 11:52:00 2026 // ai authorization authz llm rag Summary Retrieval-augmented generation makes language models far more useful by grounding them in real data, But it also raises a hard question: who is allowed to see what? This post shows how authorization can be enforced before retrieval, ensuring that RAG systems remain powerful without becoming dangerous. In the last three posts, I've been working toward a specific architectural claim. First, I argued that AI is not—and should not be—your policy engine, and that authorization must remain deterministic and external to language models.
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