Retrieval-Augmented Generation (RAG) is powerful, but it introduces a massive security risk. When your LLM is connected to private data, how do you enforce who can see what? This session shows how to secure a .NET RAG pipeline with Fine-Grained Authorization (FGA). We will move beyond simple authentication and implement a scalable, relationship-based access model using Auth0 FGA. You will learn the critical pattern of filtering vector search results based on user permissions "before" the data ever reaches the LLM. You will leave with a clear blueprint for building secure AI applications in .NET that never expose data a user shouldn't see.