Privacy-Preserving RAG by Concealing Sensitive Information from External LLMs
Research proposes concealing sensitive data before it reaches external LLMs in RAG pipelines, addressing a practical GDPR and data-sovereignty gap enterprises face when augmenting queries with internal knowledge.
Summary written by editorial AI · Source link below
arXiv:2608.12675v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) is widely used to improve the performance of Large Language Models (LLMs) in answering user queries. Existing privacy research on RAG has focused on preventing unauthorized users from accessing sensitive data. However, another important problem that is often overlooked in RAG privacy research is that external generators have access to the query and the retrieved documents, which may contain confidential infor
Editorial Analysis
Enterprises using RAG with external LLM providers risk exposing sensitive data in retrieval contexts; privacy-preserving architectures are essential for GDPR and data-sovereignty compliance.
Audit your RAG pipelines for sensitive-data exposure to external LLMs and implement pre-retrieval sanitisation controls.
RAG-based AI systems may inadvertently send sensitive data to external providers; privacy-preserving designs reduce regulatory exposure.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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