CISO's guide to RAG data security risks and protection strategies | TechTarget
Large language models can only generate responses from the data they were trained on. Yet this data might be outdated, might not include proprietary data, can cause hallucinations and could require costly updates.
That's where retrieval-augmented generation comes in. RAG overcomes these data limitations by retrieving relevant information from trusted external or internal sources at query time and providing it to the LLM as context for generating a response. Answers are more accurate, grounded in trusted source documents, based on current information and tailored to the organization's proprietary knowledge.
However, the same mechanism that makes RAG valuable -- giving AI access to enterprise data in exchange for more accurate, context-aware responses -- also creates new security risks if that data isn't properly protected. Moreover, connecting LLMs to proprietary data introduces additional risks beyond those of standalone models.
This article explains how RAG works and examines the key security risks. It...
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