Security for Retrieval Augmented Generation (RAG) Applications

Retrieval-augmented generation (RAG) is one of the most common methods for improving the contextual relevance of a LLM by connecting additional knowledge sources. Compared to other techniques like fine-tuning, RAG offers advantages in terms of speed, flexibility, and cost-effectiveness. However, there are a number of safety and security considerations which are especially critical when connected datasets contain sensitive information on a business and its customers.

In this brief, we examine the different safety and security risks that exist at every point in the lifecycle of a RAG application, including model and data poisoning, factual inconsistencies, and sensitive data leakage, as well as actionable recommendations for mitigating them in your environment.

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