AI-Native LLM Security by Vaibhav Malik (.ePUB)+
File Size: 10 MB
AI-Native LLM Security: Threats, defenses, and best practices for building safe and trustworthy AI by Vaibhav Malik, Ken Huang, Ads Dawson
Requirements: .ePUB, .PDF reader, 10 MB | True PDF, True ePUB
Overview: Unlock the secrets to safeguarding AI by exploring the top risks, essential frameworks, and cutting-edge strategies—featuring the OWASP Top 10 for LLM Applications and Generative AI. Adversarial AI attacks present a unique set of security challenges, exploiting the very foundation of how AI learns. This book explores these threats in depth, equipping cybersecurity professionals with the tools needed to secure generative AI and LLM applications. Rather than skimming the surface of emerging risks, it focuses on practical strategies, industry standards, and recent research to build a robust defense framework. Structured around actionable insights, the chapters introduce a secure-by-design methodology, integrating threat modeling and MLSecOps practices to fortify AI systems. You’ll discover how to leverage established taxonomies from OWASP, NIST, and MITRE to identify and mitigate vulnerabilities. Through real-world examples, the book highlights best practices for incorporating security controls into AI development life cycles, covering key areas such as CI/CD, MLOps, and open-access LLMs. This book is essential for cybersecurity professionals, AI practitioners, and leaders responsible for developing and securing AI systems powered by large language models. Ideal for CISOs, security architects, ML engineers, data scientists, and DevOps professionals, it provides insights on securing AI applications. Managers and executives overseeing AI initiatives will also benefit from understanding the risks and best practices outlined in this guide to ensure the integrity of their AI projects. A basic understanding of security concepts and AI fundamentals is assumed.
Genre: Non-Fiction > Tech & Devices
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