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Viser: AI-Powered Digital Cyber Resilience
AI-Powered Digital Cyber Resilience
Omar Santos og Petar Radanliev
(2026)
Sprog: Engelsk
Pearson Education, Limited
268,00 kr.
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Detaljer om varen
- Paperback: 400 sider
- Udgiver: Pearson Education, Limited (Marts 2026)
- Forfattere: Omar Santos og Petar Radanliev
- ISBN: 9780135408605
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 today's 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.
Don't 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.
Preface..
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. xix Acknowledgments..
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. xxvii About the Authors..
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. xxix 1 Understanding Digital Cyber Resilience in the Age of AI
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. 1
Chapter Objectives
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. 1 Resilience: Beyond Legacy Cybersecurity Concepts
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. 3 The NIST Four Pillars of Resilience.
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. 4 From Technical Defense to Digital Trust..
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. 12 The AI Revolution: A Duality of Threat and Defense..
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. 15 Summary.
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. 19 Test Your Skills.
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. 21 Multiple-Choice Questions
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. 21 Answers to Multiple-Choice Questions.
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. 23 Exercise/Project: Build a Cyber Resilience Playbook for AI-Era Threats
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. 24 2 Introduction to Generative AI, LLMs, and SLMs..
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. 27
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. 27 Overview of AI Technologies and Algorithms.
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. 28 Introduction to Generative AI, LLMs, and SLMs..
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. 31 The Future of AI in Cybersecurity, Emerging Trends, and Technologies..
. 35 Integration and Interoperability of AI in Cybersecurity.
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. 37 Challenges in Implementing AI Security Solutions.
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. 39 Strategies for Seamless Integration of AI and Cybersecurity.
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. 41 Integrating Malware Analysis with AI-Driven Cybersecurity.
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. 43 Summary.
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. 46 References.
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. 46 Test Your Skills.
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. 49 Multiple-Choice Questions..
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. 49 Answers to Multiple-Choice Questions.
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. 50 EXERCISES AND ANSWERS (Interview Style)
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. 51 3 Anomaly Detection, Predictive Analysis, and Threat Forecasting
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. 55 Overview of Anomaly Detection.
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. 56 Importance of Predictive Analysis in Cybersecurity..
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. 59 Machine Learning Algorithms: SVM, Decision Trees, Neural Networks.
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. 62 Statistical Methods: ARIMA, GARCH..
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. 64 Techniques for Feature Selection and Extraction..
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. 66 Metrics for Model Performance: Accuracy, Precision, Recall, F1-Score..
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. 68 Tools and Libraries for Predictive Modeling: Scikit-learn, TensorFlow.
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. 70 Integrating Dynamic Malware Analysis with Anomaly Detection and Predictive Models... 72 Summary.
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. 74 References.
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. 75 Test Your Skills.
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. 76 Multiple-Choice Questions..
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. 76 Answers to Multiple-Choice Questions.
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. 77 EXERCISES AND ANSWERS (Interview Style)
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. 78 4 AI-Driven Threat Intelligence..
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. 81
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. 81 Technical Aspects of AI in Threat Intelligence.
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. 81 Case Study: Using CNNs for Malware Classification..
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. 84 Case Study: Detecting and Analyzing Phishing Campaigns..
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. 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
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. 94 Case Study: Using MegaVul to Build an AI-Powered Vulnerability Detector.
. 95 AI Coding Agents..
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. 103 Summary.
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. 112 Test Your Skills.
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. 114 Multiple-Choice Questions
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. 114 Answers to Multiple-Choice Questions.
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. 116 EXERCISES..
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. 117 5 Introduction to AI-Driven Incident Response..
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. 121
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. 121 Foundations of Cybersecurity Incident Response.
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. 122 Understanding the Traditional Cybersecurity Incident Response Process..
. 123 The Functions of Incident Response Teams..
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. 127 The Emergence of Artificial Intelligence in Cybersecurity Incident Response.
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. 134 Summary.
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. 147 Test Your Skills.
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. 148 Multiple-Choice Questions..
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. 148 Answers to Multiple-Choice Questions.
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. 150 Project 5-1: Automated Vulnerability Triage and Patching Workflow..
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. 151 6 Real-Time Analysis, Decision-Making, Orchestration, and Automation.
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. 153 Real-Time Analysis.
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. 154 AI-Driven Decision-Making..
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. 166 The Pitfalls of AI in Security.
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. 169 Orchestration and Automation.
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. 172 The Integrated Defense: SOAR and Proactive Resilience in Practice.
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. 175 Summary.
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. 180 Test Your Skills.
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. 182 Answers to Multiple-Choice Questions.
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. 185 7 IoT Security and Cloud Security Using AI..
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. 187
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. 187 Definition of IoT and Cloud Security.
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. 188 IoT Security Challenges.
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. 191 Vulnerabilities in IoT Devices.
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. 195 Case Studies of IoT Security Breaches.
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. 197 Cloud Security Challenges..
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. 200 The Application of AI in IoT Security.
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. 202 The Application of AI in Cloud Security.
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. 206 Limitations of Using Low-Memory AI in IoT and Cloud Security.
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. 208 Future Trends in AI-Enhanced IoT and Cloud Security..
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. 210 Best Practices and Recommendations..
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. 213 Enhancing IoT and Cloud Security Using Dynamic and Static Malware Analysis.. 215 Summary.
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. 218 References.
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. 218 Test Your Skills.
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. 220 Multiple-Choice Questions
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. 220 Answers to Multiple-Choice Questions.
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. 221 EXERCISES AND ANSWERS (Interview Style)..
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. 222 8 Advanced Encryption Techniques, Privacy, and Compliance..
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. 225
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. 225 AI in Cryptography..
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. 226 Enhancing Data Security.
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. 229 Decentralization for Balancing Security with Privacy..
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. 232 Privacy-Preserving Techniques..
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. 235 Homomorphic Encryption..
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. 237 AI and Regulatory Compliance..
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. 240 Advanced Encryption Techniques and the Role of Malware in Encryption.
. 248 Summary.
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. 251 References.
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. 251 Test Your Skills.
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. 253 Multiple-Choice Questions..
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. 253 Answers to Multiple-Choice Questions.
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. 254 EXERCISES AND ANSWERS (Interview Style)
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. 255 9 Using AI to Enhance Cybersecurity Programs and Policies..
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. 257
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. 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 Cyber
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. xix Acknowledgments..
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. xxvii About the Authors..
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. xxix 1 Understanding Digital Cyber Resilience in the Age of AI
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. 1
Chapter Objectives
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. 1 Resilience: Beyond Legacy Cybersecurity Concepts
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. 3 The NIST Four Pillars of Resilience.
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. 4 From Technical Defense to Digital Trust..
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. 12 The AI Revolution: A Duality of Threat and Defense..
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. 15 Summary.
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. 19 Test Your Skills.
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. 21 Multiple-Choice Questions
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. 21 Answers to Multiple-Choice Questions.
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. 23 Exercise/Project: Build a Cyber Resilience Playbook for AI-Era Threats
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. 24 2 Introduction to Generative AI, LLMs, and SLMs..
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. 27
Chapter Objectives..
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. 27 Overview of AI Technologies and Algorithms.
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. 28 Introduction to Generative AI, LLMs, and SLMs..
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. 31 The Future of AI in Cybersecurity, Emerging Trends, and Technologies..
. 35 Integration and Interoperability of AI in Cybersecurity.
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. 37 Challenges in Implementing AI Security Solutions.
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. 39 Strategies for Seamless Integration of AI and Cybersecurity.
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. 41 Integrating Malware Analysis with AI-Driven Cybersecurity.
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. 43 Summary.
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. 46 References.
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. 46 Test Your Skills.
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. 49 Multiple-Choice Questions..
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. 49 Answers to Multiple-Choice Questions.
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. 50 EXERCISES AND ANSWERS (Interview Style)
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. 51 3 Anomaly Detection, Predictive Analysis, and Threat Forecasting
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. 55
Chapter Objectives
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. 55 Overview of Anomaly Detection.
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. 56 Importance of Predictive Analysis in Cybersecurity..
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. 59 Machine Learning Algorithms: SVM, Decision Trees, Neural Networks.
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. 62 Statistical Methods: ARIMA, GARCH..
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. 64 Techniques for Feature Selection and Extraction..
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. 66 Metrics for Model Performance: Accuracy, Precision, Recall, F1-Score..
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. 68 Tools and Libraries for Predictive Modeling: Scikit-learn, TensorFlow.
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. 70 Integrating Dynamic Malware Analysis with Anomaly Detection and Predictive Models... 72 Summary.
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. 74 References.
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. 75 Test Your Skills.
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. 76 Multiple-Choice Questions..
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. 76 Answers to Multiple-Choice Questions.
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. 77 EXERCISES AND ANSWERS (Interview Style)
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. 78 4 AI-Driven Threat Intelligence..
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. 81
Chapter Objectives
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. 81 Technical Aspects of AI in Threat Intelligence.
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. 81 Case Study: Using CNNs for Malware Classification..
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. 84 Case Study: Detecting and Analyzing Phishing Campaigns..
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. 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
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. 94 Case Study: Using MegaVul to Build an AI-Powered Vulnerability Detector.
. 95 AI Coding Agents..
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. 103 Summary.
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. 112 Test Your Skills.
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. 114 Multiple-Choice Questions
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. 114 Answers to Multiple-Choice Questions.
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. 116 EXERCISES..
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. 117 5 Introduction to AI-Driven Incident Response..
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. 121
Chapter Objectives..
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. 121 Foundations of Cybersecurity Incident Response.
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. 122 Understanding the Traditional Cybersecurity Incident Response Process..
. 123 The Functions of Incident Response Teams..
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. 127 The Emergence of Artificial Intelligence in Cybersecurity Incident Response.
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. 134 Summary.
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. 147 Test Your Skills.
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. 148 Multiple-Choice Questions..
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. 148 Answers to Multiple-Choice Questions.
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. 150 Project 5-1: Automated Vulnerability Triage and Patching Workflow..
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. 151 6 Real-Time Analysis, Decision-Making, Orchestration, and Automation.
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Chapter Objectives
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. 153 Real-Time Analysis.
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. 154 AI-Driven Decision-Making..
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. 166 The Pitfalls of AI in Security.
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. 169 Orchestration and Automation.
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. 172 The Integrated Defense: SOAR and Proactive Resilience in Practice.
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. 175 Summary.
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. 180 Test Your Skills.
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. 182 Answers to Multiple-Choice Questions.
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. 185 7 IoT Security and Cloud Security Using AI..
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. 187
Chapter Objectives..
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. 187 Definition of IoT and Cloud Security.
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. 188 IoT Security Challenges.
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. 191 Vulnerabilities in IoT Devices.
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. 195 Case Studies of IoT Security Breaches.
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. 197 Cloud Security Challenges..
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. 200 The Application of AI in IoT Security.
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. 202 The Application of AI in Cloud Security.
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. 206 Limitations of Using Low-Memory AI in IoT and Cloud Security.
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. 208 Future Trends in AI-Enhanced IoT and Cloud Security..
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. 210 Best Practices and Recommendations..
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. 213 Enhancing IoT and Cloud Security Using Dynamic and Static Malware Analysis.. 215 Summary.
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. 218 References.
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. 218 Test Your Skills.
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. 220 Multiple-Choice Questions
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. 220 Answers to Multiple-Choice Questions.
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. 221 EXERCISES AND ANSWERS (Interview Style)..
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. 222 8 Advanced Encryption Techniques, Privacy, and Compliance..
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. 225
Chapter Objectives..
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. 225 AI in Cryptography..
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. 226 Enhancing Data Security.
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. 229 Decentralization for Balancing Security with Privacy..
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. 232 Privacy-Preserving Techniques..
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. 235 Homomorphic Encryption..
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. 237 AI and Regulatory Compliance..
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. 240 Advanced Encryption Techniques and the Role of Malware in Encryption.
. 248 Summary.
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. 251 References.
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. 251 Test Your Skills.
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. 253 Multiple-Choice Questions..
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. 253 Answers to Multiple-Choice Questions.
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. 254 EXERCISES AND ANSWERS (Interview Style)
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. 255 9 Using AI to Enhance Cybersecurity Programs and Policies..
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. 257
Chapter Objectives..
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. 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 Cyber
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