Triangulating AI Security

Up-to-date reference enabling readers to address the full spectrum of AI security challenges while maintaining model utility

Generative AI Security: Defense, Threats, and Vulnerabilities delivers a technical framework for securing generative AI systems, building on established standards while focusing specifically on emerging threats to large language models and other generative AI systems. Moving beyond treating AI security as a dual-use technology, this book provides detailed technical analysis of three critical dimensions: implementing AI-powered security tools, defending against AI-enhanced attacks, and protecting AI systems from compromise through attacks like prompt injection, model poisoning, and data extraction.

The book provides concrete technical implementations supported by real-world case studies of actual AI system compromises, examining documented cases like the DeepSeek breaches, Llama vulnerabilities, and Google’s CaMeL security defenses to demonstrate attack methodologies and defense strategies while emphasizing foundational security principles that remain relevant despite technological shifts. Each chapter progresses from theoretical foundations to practical applications.

The book also includes an implementation guide and hands-on exercises focusing on specific vulnerabilities in generative AI architectures, security control implementation, and compliance frameworks.

Generative AI Security: Defense, Threats, and Vulnerabilities discusses topics including:

• Machine learning fundamentals, including supervised, unsupervised, and reinforcement learning and feature engineering and selection
• Intelligent Security Information and Event Management (SIEM), covering AI-enhanced log analysis, predictive vulnerability assessment, and automated patch generation
• Deepfakes and synthetic media, covering image and video manipulation, voice cloning, audio deepfakes, and AI’s greater impact on information integrity
• Security attacks on generative AI, including jailbreaking, adversarial, backdoor, and data poisoning attacks
• Privacy-preserving AI techniques including federated learning and homomorphic encryption

Generative AI Security: Defense, Threats, and Vulnerabilities is an essential resource for cybersecurity professionals and architects, engineers, IT professionals, and organization leaders seeking integrated strategies that address the full spectrum of Generative AI security challenges while maintaining model utility.

Shaila Rana, PhD, is a professor of Cybersecurity, co-founder of the ACT Research Institute, a cybersecurity, AI, and technology think tank, and serves as the Chair of the IEEE Standards Association initiative on Zero Trust Cybersecurity for Health Technology, Tools, Services, and Devices.

Rhonda Chicone, PhD, is a retired professor and the co-founder of the ACT Research Institute. A former CSO, CTO, and Director of Software Development, she brings decades of experience in software product development and cybersecurity.