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Author:   Shaila Rana (ACT Research Institute) ,  Rhonda Chicone (ACT Research Institute)
Publisher:   John Wiley & Sons Inc
Imprint:   Wiley-IEEE Press
ISBN:  

9781394368488

ISBN 10:   1394368488
Pages:   496
Publication Date:   18 December 2025
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   Out of stock  
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Table of Contents

Chapter 1:       Abstract          1.1: What is Generative AI?    1.2: The Evolution of AI in Cybersecurity      1.3 Overview of GAI in Security         1.4 Current Landscape of Generative AI Applications           1.5 A Triangular Approach      Chapter 1 Summary   Hypothetical Case Study: The Triangular Approach to AI Security   References     Chapter 2: Understanding Generative AI Technologies         Abstract          2.1: ML Fundamentals            2.2 Deep Learning and Neural Networks      2.3 Generative Models           2.4 NLP in Generative AI        2.5 Computer Vision in Generative AI            Conclusion      Chapter 2 Summary:  Case Study:    References     Chapter 3: Generative AI as a Security Tool  Abstract          3.1 AI-Powered Threat Detection and Response      3.2 Automated Vulnerability Discovery and Patching 3.3 Intelligent SIEMs  3.4 AI in Malware Analysis and Classification            3.5 Generative AI in Red Teaming      3.6 J-Curve for Productivity in AI-Driven Security      3.7 Regulatory Technology (RegTech)           3.8 AI for Emotional Intelligence (EQ) in Cybersecurity        Chapter 3 Summary:  Case study: GAI as a Tool      References     Chapter 4: Weaponized Generative AI           Abstract          4.1 Deepfakes and Synthetic Media   4.2 AI-Powered Social Engineering    4.3 Automated Hacking and Exploit Generation        4.4 Privacy Concerns 4.5 Weaponization of AI: Attack Vectors        4.6 Defensive Strategies Against Weaponized Generative AI           Chapter 4 Summary:  Case Study 1: Weaponized AI in a Small-Sized Organization          Case Study 2: Weaponized AI in a Large Organization        References     Chapter 5: Generative AI Systems as a Target of Cyber Threats     Abstract          5.1 Security Attacks on Generative AI            5.2 Privacy Attacks on Generative AI 5.3 Attacks on Availability       5.4 Physical Vulnerabilities     5.5 Model Extraction and Intellectual Property Risks 5.6 Model Poisoning and Supply Chain Risks           5.7 Open-Source GAI Models            5.8 Application-specific Risks 5.9 Challenges in Mitigating Generative AI Risks     Chapter 5 Summary:  Case Study 1: Small Organization - Securing Customer Chatbot Systems Case Study 2: Medium-Sized Organization - Defending Against Model Extraction Case Study 3: Large Organization - Addressing Data Poisoning in AI Training Pipelines    References     Chapter 6: Defending Against Generative AI Threats            Abstract          6.1 Deepfake Detection Techniques  6.2 Adversarial Training and Robustness      6.3 Secure AI Development Practices           6.4 AI Model Security and Protection 6.5 Privacy-Preserving AI Techniques           6.6 Proactive Threat Intelligence and AI Incident Response 6.7 MLSecOps/SecMLOPs for Secure AI Development        Chapter 6 Summary:  Case Study: Comprehensive Defense Against Generative AI Threats in a Multinational Organization       References     Chapter 7: Ethical and Regulatory Considerations    Abstract          7.1 Ethical Challenges in AI Security 7.2 AI Governance Frameworks        7.3 Current and Emerging AI Regulations     7.4 Responsible AI Development and Deployment   7.5 Balancing Innovation and Security          Chapter 7 Summary   Case Study: Navigating Ethical and Regulatory Challenges in AI Security for a Financial Institution          References     Chapter 8: Future Trends in Generative AI Security  Abstract          8.1 Quantum Computing and AI Security      8.2 Human Collaboration in Cybersecurity    8.3 Advancements in XAI       8.4 The Role of Generative AI in Zero Trust  8.5 Micromodels         8.6 AI and Blockchain 8.7 Artificial General Intelligence (AGI)          8.8 Digital Twins         8.9 Agentic AI  8.10 Multimodal models          8.11 Robotics  Chapter 8 Summary:  Case Study: Applying the Triangular Framework to Generative AI Security References     Chapter 9: Implementing Generative AI Security in Organizations   Abstract          9.1 Assessing Organizational Readiness      9.2 Developing an AI Security Strategy         9.3 Shadow AI 9.4 Building and Training AI Security Teams 9.5 Policy Recommendations for AI and Generative AI Implementation: A Triangular Approach     Chapter 9 Summary   Case Study: Implementing Generative AI Security in Organizations – A Triangular Path Forward  References     Chapter 10 Future Outlook on AI and Cybersecurity Abstract          10.1 The Evolving Role of Security Professionals     10.2 AI-Driven Incident Response and Recovery     10.3 GAI Security Triad Framework (GSTF) 10.5 Preparing for Future Challenges            10.5 Responsible AI Security Chapter 10 Summary: Practice Quiz: AI Security Triangular Framework      References     Index  

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Author Information

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.

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