AI-Powered Digital Cyber Resilience

Description

• Copyright 2026
• Dimensions: 7-3/8" x 9-1/8"
• Pages: 400
• Edition: 1st

• Book
• ISBN-10: 0-13-540860-1
• ISBN-13: 978-0-13-540860-5

Use AI to Predict, Prevent, and Protect Against the Next Cyber Attack

From cybersecurity leader Omar Santos and AI expert Dr. Petar Radanliev comes a groundbreaking guide to the future of cyber defense. This book is a practical guide to building intelligent, AI-powered cyber defenses in todays fast-evolving threat landscape. With cyber threats growing in speed, scale, and sophistication, traditional defenses can no longer keep up. This essential book shows how to use AI technologies to detect threats earlier, respond faster and build stronger digital resilience.

Designed for IT professionals, security analysts, engineers, executives, academics, and students, this guide bridges the gap between advanced AI technologies and real-world cybersecurity strategies. Whether you are managing enterprise networks, leading a security team, or preparing for a career in digital defense, this book will help you use AI to protect your most valuable assets.

As ransomware attacks, data breaches, and zero-day exploits rise, organizations must move from reactive defense to proactive resilience. This book explains how technologies such as generative AI, large language models (LLMs), and small language models (SLMs) enable real-time threat detection, automated incident response, and predictive threat analysis.

This book delivers:

• Clear explanations of AI technologies such as large language models (LLMs), generative AI, and behavior-based analytics.
• Hands-on strategies for leveraging AI to boost detection, response, recovery, and resilience.
• Case studies and practical tools to help you apply cutting-edge defense methods in real-world environments.

By reading AI-Powered Digital Cyber Resilience, you will:

• Understand the fundamentals of digital cyber resilience in an AI-driven world and why traditional security methods are no longer enough.
• Gain deep insight into generative AI, large language models (LLMs), small language models (SLMs), and how they are transforming cybersecurity.
• Apply AI-based techniques for real-time threat detection, anomaly detection, and predictive threat forecasting.
• Implement AI-driven incident response strategies, including automated orchestration and real-time decision-making.
• Secure IoT devices and cloud infrastructures using machine learning, behavioral analytics, and AI-powered access control.
• Use advanced encryption, data privacy tools, and compliance frameworks powered by intelligent automation.
• Build and enhance cybersecurity programs and policies with AI integration for better governance and risk management.
• Ensure secure and ethical AI deployments, including continuous model updates and protection against adversarial attacks.

Dont wait for the next cyberattack. Take control with the knowledge and tools you need to build lasting digital resilience. AI-Powered Digital Cyber Resilience is your essential guide to navigating and mastering the future of cybersecurity.

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Sample Content

Table of Contents

    Preface.. . . . . . . . . . . . . . . . . xix

    Acknowledgments.. . . . . . . . . . . . . . xxvii

    About the Authors.. . . . . . . . . . . . . . xxix

1 Understanding Digital Cyber Resilience in the Age of AI .. . . . 1

    Chapter Objectives . . . . . . . . . . . . . 1

    Resilience: Beyond Legacy Cybersecurity Concepts . . . . . . 3

    The NIST Four Pillars of Resilience. . . . . . . . . . 4

    From Technical Defense to Digital Trust.. . . . . . . . 12

    The AI Revolution: A Duality of Threat and Defense.. . . . . . 15

    Summary. . . . . . . . . . . . . . 19

    Test Your Skills. . . . . . . . . . . . . 21

    Multiple-Choice Questions . . . . . . . . . . . 21

    Answers to Multiple-Choice Questions. . . . . . . . . 23

    Exercise/Project: Build a Cyber Resilience Playbook for AI-Era Threats . . . 24

2 Introduction to Generative AI, LLMs, and SLMs.. . . . . . 27

    Chapter Objectives.. . . . . . . . . . . . 27

    Overview of AI Technologies and Algorithms. . . . . . . 28

    Introduction to Generative AI, LLMs, and SLMs.. . . . . . . 31

    The Future of AI in Cybersecurity, Emerging Trends, and Technologies.. . 35

    Integration and Interoperability of AI in Cybersecurity. . . . . . 37

    Challenges in Implementing AI Security Solutions. . . . . . 39

    Strategies for Seamless Integration of AI and Cybersecurity. . . . . 41

    Integrating Malware Analysis with AI-Driven Cybersecurity. . . . . 43

    Summary. . . . . . . . . . . . . . 46

    References. . . . . . . . . . . . . . 46

    Test Your Skills. . . . . . . . . . . . . 49

    Multiple-Choice Questions.. . . . . . . . . . . 49

    Answers to Multiple-Choice Questions. . . . . . . . . 50

    EXERCISES AND ANSWERS (Interview Style) . . . . . . . . 51

3 Anomaly Detection, Predictive Analysis, and Threat Forecasting .. . 55

    Chapter Objectives . . . . . . . . . . . . 55

    Overview of Anomaly Detection. . . . . . . . . . 56

    Importance of Predictive Analysis in Cybersecurity.. . . . . . 59

    Machine Learning Algorithms: SVM, Decision Trees, Neural Networks. . . 62

    Statistical Methods: ARIMA, GARCH.. . . . . . . . . 64

    Techniques for Feature Selection and Extraction.. . . . . . . 66

    Metrics for Model Performance: Accuracy, Precision, Recall, F1-Score.. . . 68

    Tools and Libraries for Predictive Modeling: Scikit-learn, TensorFlow. . . 70

    Integrating Dynamic Malware Analysis with Anomaly Detection and Predictive Models... 72

    Summary. . . . . . . . . . . . . . 74

    References. . . . . . . . . . . . . . 75

    Test Your Skills. . . . . . . . . . . . . 76

    Multiple-Choice Questions.. . . . . . . . . . . 76

    Answers to Multiple-Choice Questions. . . . . . . . . 77

    EXERCISES AND ANSWERS (Interview Style) . . . . . . . . 78

4 AI-Driven Threat Intelligence.. . . . . . . . . . 81

    Chapter Objectives . . . . . . . . . . . . 81

    Technical Aspects of AI in Threat Intelligence. . . . . . . 81

    Case Study: Using CNNs for Malware Classification.. . . . . . 84

    Case Study: Detecting and Analyzing Phishing Campaigns.. . . . . 85

    Leveraging AI to Automate STIX Document Creation for Threat Intelligence. 87

    Case Study: Automating Threat Intelligence for a Financial Institution.. . 92

    Autonomous AI Agents for Cyber Defense . . . . . . . . 94

    Case Study: Using MegaVul to Build an AI-Powered Vulnerability Detector. . 95

    AI Coding Agents.. . . . . . . . . . . . 103

    Summary. . . . . . . . . . . . . . 112

    Test Your Skills. . . . . . . . . . . . . 114

    Multiple-Choice Questions . . . . . . . . . . . 114

    Answers to Multiple-Choice Questions. . . . . . . . 116

    EXERCISES.. . . . . . . . . . . . . . 117

5 Introduction to AI-Driven Incident Response.. . . . . . 121

    Chapter Objectives.. . . . . . . . . . . . 121

    Foundations of Cybersecurity Incident Response. . . . . . 122

    Understanding the Traditional Cybersecurity Incident Response Process.. . 123

    The Functions of Incident Response Teams.. . . . . . . 127

    The Emergence of Artificial Intelligence in Cybersecurity

    Incident Response. . . . . . . . . . . . 134

    Summary. . . . . . . . . . . . . . 147

    Test Your Skills. . . . . . . . . . . . . 148

    Multiple-Choice Questions.. . . . . . . . . . . 148

    Answers to Multiple-Choice Questions. . . . . . . . 150

    Project 5-1: Automated Vulnerability Triage and Patching Workflow.. . . 151

6 Real-Time Analysis, Decision-Making, Orchestration, and Automation. .. 153

    Chapter Objectives . . . . . . . . . . . . 153

    Real-Time Analysis. . . . . . . . . . . . 154

    AI-Driven Decision-Making.. . . . . . . . . . . 166

    The Pitfalls of AI in Security. . . . . . . . . . . 169

    Orchestration and Automation. . . . . . . . . . 172

    The Integrated Defense: SOAR and Proactive Resilience in Practice. . . 175

    Summary. . . . . . . . . . . . . . 180

    Test Your Skills. . . . . . . . . . . . . 182

    Answers to Multiple-Choice Questions. . . . . . . . 185

7 IoT Security and Cloud Security Using AI.. . . . . . . 187

    Chapter Objectives.. . . . . . . . . . . . 187

    Definition of IoT and Cloud Security. . . . . . . . . 188

    IoT Security Challenges. . . . . . . . . . . 191

    Vulnerabilities in IoT Devices. . . . . . . . . . 195

    Case Studies of IoT Security Breaches. . . . . . . . . 197

    Cloud Security Challenges.. . . . . . . . . . . 200

    The Application of AI in IoT Security. . . . . . . . . 202

    The Application of AI in Cloud Security. . . . . . . . 206

    Limitations of Using Low-Memory AI in IoT and Cloud Security. . . . 208

    Future Trends in AI-Enhanced IoT and Cloud Security.. . . . . 210

    Best Practices and Recommendations.. . . . . . . . 213

    Enhancing IoT and Cloud Security Using Dynamic and Static Malware Analysis.. 215

    Summary. . . . . . . . . . . . . . 218

    References. . . . . . . . . . . . . . 218

    Test Your Skills. . . . . . . . . . . . . 220

    Multiple-Choice Questions . . . . . . . . . . . 220

    Answers to Multiple-Choice Questions. . . . . . . . 221

    EXERCISES AND ANSWERS (Interview Style).. . . . . . . 222

8 Advanced Encryption Techniques, Privacy, and Compliance.. . . 225

    Chapter Objectives.. . . . . . . . . . . . 225

    AI in Cryptography.. . . . . . . . . . . . 226

    Enhancing Data Security. . . . . . . . . . . 229

    Decentralization for Balancing Security with Privacy.. . . . . . 232

    Privacy-Preserving Techniques.. . . . . . . . . . 235

    Homomorphic Encryption.. . . . . . . . . . . 237

    AI and Regulatory Compliance.. . . . . . . . . . 240

    Advanced Encryption Techniques and the Role of Malware in Encryption. . 248

    Summary. . . . . . . . . . . . . . 251

    References. . . . . . . . . . . . . . 251

    Test Your Skills. . . . . . . . . . . . . 253

    Multiple-Choice Questions.. . . . . . . . . . . 253

    Answers to Multiple-Choice Questions. . . . . . . . 254

    EXERCISES AND ANSWERS (Interview Style) . . . . . . . 255

9 Using AI to Enhance Cybersecurity Programs and Policies.. . . 257

    Chapter Objectives.. . . . . . . . . . . . 257

    Dynamic Security Policies: Implementation and Adaptation of AI in Security Policies.. . 258

    AI-Driven Security Adjustments: Real-Time Threat Detection and Response Mechanisms.. . 264

    Enhancing the Software Development Lifecycle (SDLC): AI Integration in SDLC for Improved Security.. . 268

    AI-Powered Cybersecurity Governance: Governance Frameworks and Compliance Monitoring.. 270

    AI-Driven Security Adjustments: Real-Time Threat Detection and Response Mechanisms.. 274

    AI-Driven Integration of Malware Analysis into Cybersecurity Programs.. . 277

    Summary. . . . . . . . . . . . . . 280

    References. . . . . . . . . . . . . . 280

    Test Your Skills. . . . . . . . . . . . . 281

    Multiple-Choice Questions.. . . . . . . . . . . 281

    Answers to Multiple-Choice Questions. . . . . . . . 282

    EXERCISES AND ANSWERS.. . . . . . . . . . . 283

10 Securing AI Implementations.. . . . . . . . . . 285

    Chapter Objectives.. . . . . . . . . . . . 285

    The Coalition for Secure AI.. . . . . . . . . . . 286

    The NIST AI Risk Management Framework.. . . . . . . . 287

    Threat Modeling AI Systems.. . . . . . . . . . 288

    Adversarial Machine Learning (AML). . . . . . . . . 318

    Securing Agentic AI and Multi-Agent Systems (MAS). . . . . . 320

    Red Teaming in AI Systems.. . . . . . . . . . . 323

    Continuous Monitoring and Observability.. . . . . . . . 328

    Summary. . . . . . . . . . . . . . 331

    Test Your Skills. . . . . . . . . . . . . 332

    Multiple-Choice Questions.. . . . . . . . . . . 332

    Answers to Multiple-Choice Questions. . . . . . . . 336

    Project 10-1: A Playbook for a Hybrid Human-AI Security Team. . . . 338

9780135408605, TOC, 12/9/2025

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