List of Figures xxiii -- List of Tables xxv -- Endorsements xxvii -- About the Author xxxi -- Preface xxxiii -- Acknowledgements xxxv -- 1 Introduction 1 -- 1.1 Artificial Intelligence (AI) 1 -- 1.1.1 Narrow AI (Weak AI) 2 -- 1.1.2 General AI (Strong AI) 2 -- 1.2 Machine Learning (ML) 3 -- 1.3 Deep Learning 3 -- 1.4 Generative AI 4 -- 1.4.1 GenAI vs. Other AI 5 -- 1.5 Cybersecurity 6 -- 1.6 Ethics 7 -- 1.7 AI to GenAI: Milestones and Evolutions 8 -- 1.7.1 1950s: Foundations of AI 8 -- 1.7.2 1960s: Early AI Developments 9 -- 1.7.3 1970s-1980s: AI Growth and AI Winter 9 -- 1.7.4 1990s: New Victory 9 -- 1.7.5 2010s: Rise of GenAI 10 -- 1.8 AI in Cybersecurity 10 -- 1.8.1 Advanced Threat Detection and Prevention 10 -- 1.8.2 Real-Time Adaptation and Responsiveness 11 -- 1.8.3 Behavioral Analysis and Anomaly Detection 11 -- 1.8.4 Phishing Mitigation 11 -- 1.8.5 Harnessing Threat Intelligence 11 -- 1.8.6 GenAI in Cybersecurity 12 -- 1.9 Introduction to Ethical Considerations in GenAI 12 -- 1.9.1 Bias and Fairness 12 -- 1.9.2 Privacy 12 -- 1.9.3 Transparency and Explainability 13 -- 1.9.4 Accountability and Responsibility 13 -- 1.9.5 Malicious Use 13 -- 1.9.6 Equity and Access 13 -- 1.9.7 Human Autonomy and Control 14 -- 1.10 Overview of the Regional Regulatory Landscape for GenAI 14 -- 1.10.1 North America 14 -- 1.10.2 Europe 15 -- 1.10.3 Asia 15 -- 1.10.4 Africa 15 -- 1.10.5 Australia 15 -- 1.11 Tomorrow 15 -- 2 Cybersecurity: Understanding the Digital Fortress 17 -- 2.1 Different Types of Cybersecurity 17 -- 2.1.1 Network Security 17 -- 2.1.2 Application Security 19 -- 2.1.3 Information Security 20 -- 2.1.4 Operational Security 21 -- 2.1.5 Disaster Recovery and Business Continuity 22 -- 2.1.6 Endpoint Security 22 -- 2.1.7 Identity and Access Management (IAM) 23 -- 2.1.8 Cloud Security 24 -- 2.1.9 Mobile Security 24 -- 2.1.10 Critical Infrastructure Security 24 -- 2.1.11 Physical Security 25 -- 2.2 Cost of Cybercrime 25 -- 2.2.1 Global Impact 25 -- 2.2.2 Regional Perspectives 27 -- 2.2.2.1 North America 27 -- 2.2.2.2 Europe 28 -- 2.2.2.3 Asia 28 -- 2.2.2.4 Africa 28 -- 2.2.2.5 Latin America 29 -- 2.3 Industry-Specific Cybersecurity Challenges 30 -- 2.3.1 Financial Sector 30 -- 2.3.2 Healthcare 30 -- 2.3.3 Government 31 -- 2.3.4 E-Commerce 31 -- 2.3.5 Industrial and Critical Infrastructure 32 -- 2.4 Current Implications and Measures 32 -- 2.5 Roles of AI in Cybersecurity 33 -- 2.5.1 Advanced Threat Detection and Anomaly Recognition 33 -- 2.5.2 Proactive Threat Hunting 34 -- 2.5.3 Automated Incident Response 34 -- 2.5.4 Enhancing IoT and Edge Security 34 -- 2.5.5 Compliance and Data Privacy 35 -- 2.5.6 Predictive Capabilities in Cybersecurity 35 -- 2.5.7 Real-Time Detection and Response 35 -- 2.5.8 Autonomous Response to Cyber Threats 36 -- 2.5.9 Advanced Threat Intelligence 36 -- 2.6 Roles of GenAI in Cybersecurity 36 -- 2.7 Importance of Ethics in Cybersecurity 37 -- 2.7.1 Ethical Concerns of AI in Cybersecurity 37 -- 2.7.2 Ethical Concerns of GenAI in Cybersecurity 38 -- 2.7.3 Cybersecurity-Related Regulations: A Global Overview 39 -- 2.7.3.1 United States 39 -- 2.7.3.2 Canada 39 -- 2.7.3.3 United Kingdom 41 -- 2.7.3.4 European Union 42 -- 2.7.3.5 Asia-Pacific 42 -- 2.7.3.6 Australia 43 -- 2.7.3.7 India 43 -- 2.7.3.8 South Korea 43 -- 2.7.3.9 Middle East and Africa 43 -- 2.7.3.10 Latin America 44 -- 2.7.4 UN SDGs for Cybersecurity 45 -- 2.7.5 Use Cases for Ethical Violation of GenAI Affecting Cybersecurity 46 -- 2.7.5.1 Indian Telecom Data Breach 46 -- 2.7.5.2 Hospital Simone Veil Ransomware Attack 46 -- 2.7.5.3 Microsoft Azure Executive Accounts Breach 46 -- 3 Understanding GenAI 47 -- 3.1 Types of GenAI 48 -- 3.1.1 Text Generation 49 -- 3.1.2 Natural Language Understanding (NLU) 49 -- 3.1.3 Image Generation 49 -- 3.1.4 Audio and Speech Generation 50 -- 3.1.5 Music Generation 50 -- 3.1.6 Video Generation 50 -- 3.1.7 Multimodal Generation 50 -- 3.1.8 Drug Discovery and Molecular Generation 51 -- 3.1.9 Synthetic Data Generation 51 -- 3.1.10 Predictive Text and Autocomplete 51 -- 3.1.11 Game Content Generation 52 -- 3.2 Current Technological Landscape 52 -- 3.2.1 Advancements in GenAI 52 -- 3.2.2 Cybersecurity Implications 52 -- 3.2.3 Ethical Considerations 54 -- 3.3 Tools and Frameworks 54 -- 3.3.1 Deep Learning Frameworks 54 -- 3.4 Platforms and Services 56 -- 3.5 Libraries and Tools for Specific Applications 58 -- 3.6 Methodologies to Streamline Life Cycle of GenAI 60 -- 3.6.1 Machine Learning Operations (MLOps) 60 -- 3.6.2 AI Operations (AIOps) 62 -- 3.6.3 MLOps vs. AIOps 63 -- 3.6.4 Development and Operations (DevOps) 65 -- 3.6.5 Data Operations (DataOps) 66 -- 3.6.6 ModelOps 67 -- 3.7 A Few Common Algorithms 67 -- 3.7.1 Generative Adversarial Networks 67 -- 3.7.2 Variational Autoencoders (VAEs) 69 -- 3.7.3 Transformer Models 70 -- 3.7.4 Autoregressive Models 70 -- 3.7.5 Flow-Based Models 71 -- 3.7.6 Energy-Based Models (EBMs) 71 -- 3.7.7 Diffusion Models 71 -- 3.7.8 Restricted Boltzmann Machines (RBMs) 72 -- 3.7.9 Hybrid Models 72 -- 3.7.10 Multimodal Models 72 -- 3.8 Validation of GenAI Models 73 -- 3.8.1 Quantitative Validation Techniques 73 -- 3.8.2 Advanced Statistical Validation Methods 76 -- 3.8.3 Qualitative and Application-Specific Evaluation 77 -- 3.9 GenAI in Actions 78 -- 3.9.1 Automated Journalism 78 -- 3.9.2 Personalized Learning Environments 78 -- 3.9.3 Predictive Maintenance in Manufacturing 79 -- 3.9.4 Drug Discovery 79 -- 3.9.5 Fashion Design 80 -- 3.9.6 Interactive Chatbots for Customer Service 80 -- 3.9.7 Generative Art 80 -- 4 GenAI in Cybersecurity 83 -- 4.1 The Dual-Use Nature of GenAI in Cybersecurity 83 -- 4.2 Applications of GenAI in Cybersecurity 84 -- 4.2.1 Anomaly Detection 84 -- 4.2.2 Threat Simulation 85 -- 4.2.3 Automated Security Testing 86 -- 4.2.4 Phishing Email Creation for Training 86 -- 4.2.5 Cybersecurity Policy Generation 86 -- 4.2.6 Deception Technologies 86 -- 4.2.7 Threat Modeling and Prediction 87 -- 4.2.8 Customized Security Measures 87 -- 4.2.9 Report Generation and Incident Reporting Compliance 87 -- 4.2.10 Creation of Dynamic Dashboards 87 -- 4.2.11 Analysis of Cybersecurity Legal Documents 88 -- 4.2.12 Training and Simulation 88 -- 4.2.13 GenAI for Cyber Defense for Satellites 88 -- 4.2.14 Enhanced Threat Detection 88 -- 4.2.15 Automated Incident Response 89 -- 4.3 Potential Risks and Mitigation Methods 89 -- 4.3.1 Risks 89 -- 4.3.1.1 AI-Generated Phishing Attacks 89 -- 4.3.1.2 Malware Development 89 -- 4.3.1.3 Adversarial Attacks Against AI Systems 90 -- 4.3.1.4 Creation of Evasive Malware 91 -- 4.3.1.5 Deepfake Technology 91 -- 4.3.1.6 Automated Vulnerability Discovery 91 -- 4.3.1.7 AI-Generated Disinformation 91 -- 4.3.2 Risk Mitigation Methods for GenAI 91 -- 4.3.2.1 Technical Solutions 92 -- 4.3.2.2 Incident Response Planning 94 -- 4.4 Infrastructure for GenAI in Cybersecurity 96 -- 4.4.1 Technical Infrastructure 96 -- 4.4.1.1 Computing Resources 96 -- 4.4.1.2 Data Storage and Management 98 -- 4.4.1.3 Networking Infrastructure 99 -- 4.4.1.4 High-Speed Network Interfaces 100 -- 4.4.1.5 AI Development Platforms 101 -- 4.4.1.6 GenAI-Cybersecurity Integration Tools 102 -- 4.4.2 Organizational Infrastructure 104 -- 4.4.2.1 Skilled Workforce 104 -- 4.4.2.2 Training and Development 105 -- 4.4.2.3 Ethical and Legal Framework 106 -- 4.4.2.4 Collaboration and Partnerships 107 -- 5 Foundations of Ethics in GenAI 111 -- 5.1 History of Ethics in GenAI-Related Technology 111 -- 5.1.1 Ancient Foundations 111 -- 5.1.2 The Industrial Era 112 -- 5.1.3 20th Century 113 -- 5.1.4 The Rise of Computers and the Internet 113 -- 5.1.5 21st Century: The Digital Age 113 -- 5.1.6 Contemporary Ethical Frameworks 113 -- 5.2 Basic Ethical Principles and Theories 113 -- 5.2.1 Metaethics 114 -- 5.2.2 Normative Ethics 114 -- 5.2.3 Applied Ethics 115 -- 5.3 Existing Regulatory Landscape: The Role of International Standards and Agreements 115 -- 5.3.1 ISO/IEC Standards 116 -- 5.3.1.1 For Cybersecurity 116 -- 5.3.1.2 For AI 117 -- 5.3.1.3 Loosely Coupled with GenAI 118 -- 5.3.2 EU Ethics Guidelines 118 -- 5.3.3 UNESCO Recommendations 119 -- 5.3.4 OECD Principles on AI 119 -- 5.3.5 G7 and G20 Summits 121 -- 5.3.6 IEEE's Ethically Aligned Design 121 -- 5.3.7 Asilomar AI Principles 121 -- 5.3.8 AI4People's Ethical Framework 122 -- 5.3.9 Google's AI Principles 123 -- 5.3.10 Partnership on AI 123 -- 5.4 Why Separate Ethical Standards for GenAI? 124 -- 5.5 United Nation's Sustainable Development Goals 125 -- 5.5.1 For

Cybersecurity 125 -- 5.5.2 For AI 125 -- 5.5.3 For GenAI 127 -- 5.5.4 Alignment of Standards with SDGs for AI, GenAI, and Cybersecurity 127 -- 5.6 Regional Approaches: Policies for AI in Cybersecurity 128 -- 5.6.1 North America 128 -- 5.6.1.1 The United States of America 128 -- 5.6.1.2 Canada 131 -- 5.6.2 Europe 131 -- 5.6.2.1 EU Cybersecurity Strategy 131 -- 5.6.2.2 United States vs. EU 134 -- 5.6.2.3 United Kingdom 134 -- 5.6.3 Asia 135 -- 5.6.3.1 China 135 -- 5.6.3.2 Japan 136 -- 5.6.3.3 South Korea 136 ...