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 AI

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Key Features:

- Understand adversarial AI attacks to strengthen your AI security posture effectively

- Leverage insights from LLM security experts to navigate emerging threats and challenges

- Implement secure-by-design strategies and MLSecOps practices for robust AI system protection

- Purchase of the print or Kindle book includes a free PDF eBook

Book Description:

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.

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.

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What You Will Learn:

- Understand unique security risks posed by LLMs

- Identify vulnerabilities and attack vectors using threat modeling

- Detect and respond to security incidents in operational LLM deployments

- Navigate the complex legal and ethical landscape of LLM security

- Develop strategies for ongoing governance and continuous improvement

- Mitigate risks across the LLM life cycle, from data curation to operations

- Design secure LLM architectures with isolation and access controls

Who this book is for:

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.

Table of Contents

- Fundamentals and Introduction to Large Language Models

- Securing Large Language Models

- The Dual Nature of LLM Risks: Inherent Vulnerabilities and Malicious Actors

- Mapping Trust Boundaries in LLM Architectures

- Aligning LLM Security with Organizational Objectives and Regulatory Landscapes

- Identifying and Prioritizing LLM Security Risks with OWASP

- Diving Deep: Profiles of the Top 10 LLM Security Risks

- Mitigating LLM Risks: Strategies and Techniques for Each OWASP Category

(N.B. Please use the Read Sample option to see further chapters)