Building a Secure RAG Pipeline on AWS: A Step-by-Step Implementation Guide

When you are connecting your company’s internal data to Large Language models through RAG, APIs, SQL, etc., are you sure that it is completely safe? There might be contracts signed with the LLM providers, that your data should not be used for any training or auditing, but is that all enough? Can there be attacks? Is there a chance for your data to be compromised? Well, the answer is Yes. The RAG pipelines that you build, if contains sensitive information such as customer records, financial data, personally identifiable information, and if the data flows to a third-party model provider outside your network, then your data goes out of your network with every single query. The convenience of natural language access to enterprise data comes with a security cost that many organizations underestimate. The problem is straightforward: RAG retrieves text chunks from a knowledge base and passes them directly to an LLM as context. If those chunks contain credit card numbers, customer names, or ot