Build secure RAG applications with AWS serverless data lakes

Data is your generative AI differentiator, and successful generative AI implementation depends on a robust data strategy incorporating a comprehensive data governance approach. Traditional data architectures often struggle to meet the unique demands of generative such as applications. An effective generative AI data strategy requires several key components like seamless integration of diverse data sources, real-time processing capabilities, comprehensive data governance frameworks that maintain data quality and compliance, and secure access patterns that respect organizational boundaries. In particular, Retrieval Augmented Generation (RAG) applications have emerged as one of the most promising developments in this space. RAG is the process of optimizing the output of a foundation model (FM), so it references a knowledge base outside of its training data sources before generating a response. Such systems require secure, scalable, and flexible data ingestion and access patterns to enterprise data.

In this post, we explore how to build a secure RAG ...