This session explores building sensitive data protection directly into Retrieval-Augmented Generation (RAG) architectures. We'll demonstrate how to leverage Cloud Data Loss Prevention (Cloud DLP) and the Faker Library to anonymize sensitive data within the RAG pipeline. The session will cover techniques for reversible transformations using Memorystore and Firestore for data mapping, and discuss integrating these methods with Large Language Models (LLMs) like Gemini via LangChain and Vertex AI Search. Learn how to create secure and compliant AI solutions that protect sensitive data and adhere to regulations like the EU AI Act.