LLM apps fail in predictable ways: prompt injection crosses trust boundaries, RAG retrieves the wrong thing (or too much), secrets leak through tooling, and teams discover incidents only after users report them. This session shows how to design an LLM application on Microsoft Azure with a zero-trust mindset, treating prompts, retrieval content, tools, and model outputs as untrusted inputs, and how to translate that into concrete Azure controls.