AI-Enabled Cyber Threats:

SANDEEP DOMMARI is a recognized Cybersecurity, IAM, and AI leader with over 18 years of experience. Currently serving as a Principal Cybersecurity Architect, he designs scalable, secure digital ecosystems that protect millions globally.
Throughout his career across the public and private sectors, Sandeep has spearheaded large-scale IAM transformations and AI-driven threat detection initiatives. A passionate advocate for ethical AI and zero-trust security, he bridges cutting-edge technology with practical enterprise needs to develop proactive, resilient architectures.
His mission is to build secure, trusted digital environments that serve both business and society.

Preface xi
Acknowledgements xvii
About the Companion Website xix

1 Introduction to Artificial Intelligence-enabled Cyber Threats 1
1.1 The Convergence of Artificial Intelligence and Cybersecurity 1
1.2 Historical Evolution of Cyber Threats 3
1.3 The AI Revolution in Cybercrime 5
1.4 Defining AI-enabled Threats 6
1.5 Scope and Scale of the Problem 8
1.6 Current Threat Landscape 9
1.7 Objectives of This Book 11
1.8 Target Audience and Structure 13
1.9 Terminology and Conventions 15
1.10 Looking Ahead 17

2 Fundamentals of Artificial Intelligence/Machine Learning in Cybersecurity Context 21
2.1 Machine Learning Basics 21
2.2 Supervised Learning Algorithms 23
2.3 Unsupervised Learning Algorithms 25
2.4 Deep Learning and Neural Networks 27
2.5 Natural Language Processing 30
2.6 Computer Vision and Generative Adversarial Networks 32
2.7 Reinforcement Learning 35
2.8 AI in Offensive Cybersecurity 37
2.9 AI in Defensive Cybersecurity 39
2.10 Code Examples and Implementations 41

3 Artificial Intelligence-enhanced Attack Vectors 47
3.1 AI-generated Phishing and Social Engineering 47
3.2 Large Language Models in Cybercrime 49
3.3 Spear-phishing Automation 53
3.4 Deepfake Technology 56
3.5 Voice Cloning Attacks 60
3.6 Detection Evasion Techniques 64
3.7 Case Studies and Statistics 67

4 Artificial Intelligence-powered Malware and Ransomware 75
4.1 Polymorphic Malware Using Machine Learning 75
4.2 Adversarial Machine Learning for Evasion 80
4.3 AI-generated Code Obfuscation 84
4.4 Ransomware-as-a-service with AI 88
4.5 Automated Vulnerability Exploitation 94
4.6 Major Ransomware Campaigns 98
4.7 Malicious AI Tools and Frameworks 104

5 Adversarial Machine Learning Attacks 113
5.1 Data Poisoning and Backdoor Attacks 113
5.2 Evasion Attacks and Adversarial Examples 116
5.3 Model Inversion and Privacy Attacks 119
5.4 Model Extraction and Stealing 122
5.5 Prompt Injection and LLM Attacks 125
5.6 AI System Vulnerabilities 128
5.7 Defense Mechanisms 130

6 Real-world Case Studies and Forensic Analysis 137
6.1 MGM Resorts Cyberattack 137
6.2 Colonial Pipeline Ransomware 141
6.3 Arup Engineering Deepfake Fraud 142
6.4 Supplementary Detailed Analysis 143
6.5 Threat Actor Profiles and TTPs 154

7 Defensive Artificial Intelligence Technologies and Countermeasures 159
7.1 Machine Learning-based Threat Detection 159
7.2 Anomaly Detection Algorithms 161
7.3 Behavioral Analytics and UEBA 164
7.4 Network Traffic Analysis (AI/ML Approaches) 165
7.5 AI-powered IDS/IPS Systems 167
7.6 Automated Threat Hunting 169
7.7 SIEM and SOAR with AI 171
7.8 Zero Trust Architecture 172
7.9 Deception Technologies 175
7.10 Real-world Implementations 176

8 Technical Implementation-Code Examples and Frameworks 185
8.1 TensorFlow for Cybersecurity 185
8.2 PyTorch for Threat Detection 187
8.3 Scikit-learn for Security Analytics 189
8.4 Building AI-powered IDS 191
8.5 Implementing Behavioral Analytics 194
8.6 Automated Response Systems 196
8.7 Adversarial Training 198
8.8 Model Hardening Techniques 200
8.9 Explainable AI for Security 202
8.10 MLOps for Security Deployment 204

9 Policy, Ethics, and Governance 211
9.1 Regulatory Frameworks 211
9.2 NIST AI Risk Management Framework 212
9.3 EU AI Act Implications 214
9.4 Ethical Considerations in AI-enabled Cyber Operations 217
9.5 Bias and Fairness in AI Security Systems 218
9.6 Privacy and Data Protection: GDPR, CCPA, and Beyond 220
9.7 Accountability and Transparency in AI Systems 221
9.8 AI Governance Framework for Cybersecurity 223
9.9 International Cooperation and Agreements 225
9.10 Industry Best Practices and Self-regulation 226

10 Future Trends and Emerging Threats 231
10.1 Introduction to the Future of AI in Cybercrime 231
10.2 The Quantum Computing Threat to Cryptography 232
10.3 PQC and the Transition 234
10.4 Next-generation AI Attacks: Swarm Intelligence and Self-evolving Malware 236
10.5 The Rise of Autonomous AI Agents in Cyber Warfare 238
10.6 Securing the Convergence: AI in IoT, Edge, 5G, and 6G Networks 240
10.7 The Role of Blockchain in Future Cybersecurity Architectures 242
10.8 Nation-state Cyber Warfare in the AI Era 243
10.9 Predictions for the Cyber Threat Landscape (2025-2030) 245
10.10 Conclusion: Recommendations for Organizational Resilience 247

References 248
Subject Index 253