"AI Native LLM Security" is your essential guide to understanding and securing large language models and AI systems. With a focus on implementing practical strategies and leveraging frameworks like OWASP Top 10, this book equips professionals to identify and mitigate risks effectively. By reading this, you'll gain the expertise to confidently manage LLM security challenges. What this Book will help me do Learn about adversarial AI attacks and methods to defend against them. Understand secure-by-design methodologies and their application to LLM systems. Gain insights on implementing MLSecOps practices for robust AI security. Navigate ethical considerations and legal aspects of AI security. Secure AI development life cycles with practical strategies and standards. Author(s) The authors, Vaibhav Malik, Ken Huang, and Adam Dawson, are experts in AI security with collective experience covering cybersecurity, AI development, and security frameworks. Their dedication to advancing trustworthy AI ensures that this book is both technically comprehensive and approachable. Who is it for? This book is perfect for cybersecurity experts, AI developers, and technology managers aiming to secure and manage AI systems. Readers should have a basic understanding of AI and security concepts. If you're a security architect, ML engineer, DevOps professional, or a leader overseeing AI initiatives, this book will help you address LLM security effectively for your field.