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
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 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 like 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.What you will learn
Understand unique security risks posed by large language models
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 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.
Sprache
Verlagsort
Maße
Höhe: 235 mm
Breite: 191 mm
ISBN-13
978-1-83620-375-9 (9781836203759)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
Vaibhav Malik is a cybersecurity expert with over 12 years of experience in networking and security. As a Partner Solutions Architect at Cloudflare, he designs and implements effective security solutions for global partners. Vaibhav is a recognized industry thought leader in Zero Trust Security Architecture and holds an M.S. in Telecommunications from the University of Colorado Boulder and an M.B.A. from the University of Illinois Urbana-Champaign. His extensive expertise in AI security and his practical experience in designing scalable AI infrastructure make him uniquely qualified to guide readers through the complex landscape of LLM security. Ken Huang is a renowned AI expert, serving as co-chair of AI Safety Working Groups at Cloud Security Alliance and the AI STR Working Group at World Digital Technology Academy under the UN Framework. As CEO of DistributedApps, he provides specialized GenAI consulting.
A key contributor to OWASP's Top 10 Risks for LLM Applications and NIST's Generative AI Working Group, Huang has authored influential books including Beyond AI (Springer, 2023), Generative AI Security (Springer, 2024), and Agentic AI: Theories and Practice (Springer, 2025)
He's a global speaker at prestigious events such as Davos WEF, ACM, IEEE, and RSAC. Huang is also a member of the OpenAI Forum and project leader for the OWASP AI Vulnerability Scoring System project. Ads Dawson is a seasoned AI Full-Stack Red Teamer and Staff AI Security Researcher at Dreadnode, boasting extensive expertise in red teaming, ethical hacking, application security engineering, and architecture, particularly in NLP security. As the Technical Lead and founder of the OWASP Top 10 for LLM Applications project and contributing to the MITRE CWE AI Working Group, Ads has played a pivotal role in shaping the industry's benchmarks for LLM security best practices. Committed to fostering hands-on learning and practical application, Ads is dedicated to empowering readers to identify and effectively mitigate LLM security risks within authentic, real-world scenarios.
Table of Contents
Introduction to Large Language Models and AI Security
Securing Large Language Models in Practice
The Dual Nature of LLM Risks: Inherent Vulnerabilities and Malicious Actors
Key Trust Boundaries and Attack Surfaces in LLM Systems
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
Adapting the OWASP Top 10 to Diverse LLM Use Cases and Deployment Scenarios
Designing LLM Systems for Security: Architecture, Controls, and Best Practices
Integrating Security into the LLM Development Lifecycle: From Data Curation to Deployment
Operational Resilience: Monitoring, Incident Response, and Continuous Improvement
The Future of LLM Security: Emerging Threats, Promising Defenses, and the Path Forward
