Large Language Models (LLMs) are transforming businesses, but they are also opening a Pandora’s box of unique security vulnerabilities. From clever prompt injections and jailbreaks to data poisoning and indirect attacks, traditional application security measures are no longer enough to keep systems safe. To build truly resilient AI applications, we must learn to think like the adversaries. In this session, we will dive deep into the world of AI Red Teaming—the practice of intentionally challenging, probing, and attacking LLMs to uncover flaws before malicious actors do. We will move past the basic concepts of system prompts and explore advanced methodologies for model hardening and defense-in-depth. Through practical examples and real-world scenarios, we will examine how automated and manual red teaming can expose hidden risks, and how to implement robust guardrails that don't compromise model performance. Whether you are a security professional, a data scientist, or an engineer deploying AI to production, this talk will provide you with a blueprint for securing the next generation of intelligent software.