AI-Native LLM Security: Threats, defenses, and best practices for building safe and trustworthy AI

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 AIDRM-free PDF version + access to Packt's next-gen Reader*Key FeaturesUnderstand adversarial AI attacks to strengthen your AI security posture effectivelyLeverage insights from LLM security experts to navigate emerging threats and challengesImplement secure-by-design strategies and MLSecOps practices for robust AI system protectionPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionAdversarial 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.Built on the expertise of its co-authors—pioneers in the OWASP Top 10 for LLM applications—this guide also addresses the ethical implications of AI security, contributing to the broader conversation on trustworthy AI. By the end of this book, you’ll be able to develop, deploy, and secure AI technologies with confidence and clarity.*Email sign-up and proof of purchase requiredWhat you will learnUnderstand unique security risks posed by LLMsIdentify vulnerabilities and attack vectors using threat modelingDetect and respond to security incidents in operational LLM deploymentsNavigate the complex legal and ethical landscape of LLM securityDevelop strategies for ongoing governance and continuous improvementMitigate risks across the LLM life cycle, from data curation to operationsDesign secure LLM architectures with isolation and access controlsWho this book is forThis 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.Table of ContentsFundamentals and Introduction to Large Language ModelsSecuring Large Language ModelsThe Dual Nature of LLM Risks: Inherent Vulnerabilities and Malicious ActorsMapping Trust Boundaries in LLM ArchitecturesAligning LLM Security with Organizational Objectives and Regulatory LandscapesIdentifying and Prioritizing LLM Security Risks with OWASPDiving Deep: Profiles of the Top 10 LLM Security RisksMitigating LLM Risks: Strategies and Techniques for Each OWASP Category(N.B. Please use the Read Sample option to see further chapters) Read more