Authorization Before Retrieval: Making RAG Safe by Construction (www.windley.com)

A recent development in retrieval-augmented generation (RAG) emphasizes the importance of incorporating authorization at the retrieval stage, rather than treating it merely as an afterthought during prompt generation. This post outlines a new architecture that ensures language models only access data they are authorized to view, addressing a crucial concern in AI/ML about data privacy and security. By utilizing a method called Cedar's type-aware partial evaluation (TPE), the system evaluates authorization policies against abstract resources, creating policy residuals that act as constraints for vector database queries. This innovation allows for a precise filtering of context that aligns with user permissions before any information reaches the language model. The significance of this approach lies in its potential to enhance the safety and accountability of RAG systems, especially in sensitive domains such as finance, where unauthorized access to confidential information can have serious consequences. By ensuring that the retrieval process itself is governed by strict authorization mechanisms, developers can maintain the system's power without risking security. This definitive separation between authorization, data retrieval, and prompt generation creates a more robust and defensible framework for implementing AI applications, allowing for dynamic interactions while firmly controlling access to sensitive data.