AI-Powered Digital Cyber Resilience

Häftad, Engelska, 2026

273 kr

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Beskrivning

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.

Produktinformation

Utgivningsdatum:2026-03-04
Mått:186 x 232 x 19 mm
Vikt:658 g
Format:Häftad
Språk:Engelska
Antal sidor:400
Upplaga:1
Förlag:Pearson Education
ISBN:9780135408605

Utforska kategorier

Artificiell intelligens inom Data och IT

Mer om författaren

Omar Santos, a cybersecurity leader and Distinguished Engineer at Cisco, is renowned for his expertise in ethical hacking, vulnerability research, AI security, and incident response. He has led the DEF CON’s Red Team Village, co-chaired the Coalition of Secure AI (CoSAI), and is a prolific author. Omar's contributions have significantly advanced technology and global security standards.Petar Radanliev obtained his PhD at the University of Wales in 2014 before becoming a Post-Doctoral Research Associate at the Department of Computer Science at the University of Oxford. His current research focuses on artificial intelligence, cybersecurity, quantum computing, and blockchain technology. Before joining academia, Dr. Petar Radanliev spent ten years as a Cybersecurity Manager for RBS--the largest bank in the world at the time--and five years as a Lead Penetration Tester for the Ministry for Defence.

Innehållsförteckning

Preface.. . . . . . . . . . . . . . . . . xixAcknowledgments.. . . . . . . . . . . . . . xxviiAbout the Authors.. . . . . . . . . . . . . . xxix1 Understanding Digital Cyber Resilience in the Age of AI .. . . . 1Chapter Objectives . . . . . . . . . . . . . 1Resilience: Beyond Legacy Cybersecurity Concepts . . . . . . 3The NIST Four Pillars of Resilience. . . . . . . . . . 4From Technical Defense to Digital Trust.. . . . . . . . 12The AI Revolution: A Duality of Threat and Defense.. . . . . . 15Summary. . . . . . . . . . . . . . 19Test Your Skills. . . . . . . . . . . . . 21Multiple-Choice Questions . . . . . . . . . . . 21Answers to Multiple-Choice Questions. . . . . . . . . 23Exercise/Project: Build a Cyber Resilience Playbook for AI-Era Threats . . . 242 Introduction to Generative AI, LLMs, and SLMs.. . . . . . 27Chapter Objectives.. . . . . . . . . . . . 27Overview of AI Technologies and Algorithms. . . . . . . 28Introduction to Generative AI, LLMs, and SLMs.. . . . . . . 31The Future of AI in Cybersecurity, Emerging Trends, and Technologies.. . 35Integration and Interoperability of AI in Cybersecurity. . . . . . 37Challenges in Implementing AI Security Solutions. . . . . . 39Strategies for Seamless Integration of AI and Cybersecurity. . . . . 41Integrating Malware Analysis with AI-Driven Cybersecurity. . . . . 43Summary. . . . . . . . . . . . . . 46References. . . . . . . . . . . . . . 46Test Your Skills. . . . . . . . . . . . . 49Multiple-Choice Questions.. . . . . . . . . . . 49Answers to Multiple-Choice Questions. . . . . . . . . 50EXERCISES AND ANSWERS (Interview Style) . . . . . . . . 513 Anomaly Detection, Predictive Analysis, and Threat Forecasting .. . 55Chapter Objectives . . . . . . . . . . . . 55Overview of Anomaly Detection. . . . . . . . . . 56Importance of Predictive Analysis in Cybersecurity.. . . . . . 59Machine Learning Algorithms: SVM, Decision Trees, Neural Networks. . . 62Statistical Methods: ARIMA, GARCH.. . . . . . . . . 64Techniques for Feature Selection and Extraction.. . . . . . . 66Metrics for Model Performance: Accuracy, Precision, Recall, F1-Score.. . . 68Tools and Libraries for Predictive Modeling: Scikit-learn, TensorFlow. . . 70Integrating Dynamic Malware Analysis with Anomaly Detection and Predictive Models... 72Summary. . . . . . . . . . . . . . 74References. . . . . . . . . . . . . . 75Test Your Skills. . . . . . . . . . . . . 76Multiple-Choice Questions.. . . . . . . . . . . 76Answers to Multiple-Choice Questions. . . . . . . . . 77EXERCISES AND ANSWERS (Interview Style) . . . . . . . . 784 AI-Driven Threat Intelligence.. . . . . . . . . . 81Chapter Objectives . . . . . . . . . . . . 81Technical Aspects of AI in Threat Intelligence. . . . . . . 81Case Study: Using CNNs for Malware Classification.. . . . . . 84Case Study: Detecting and Analyzing Phishing Campaigns.. . . . . 85Leveraging AI to Automate STIX Document Creation for Threat Intelligence. 87Case Study: Automating Threat Intelligence for a Financial Institution.. . 92Autonomous AI Agents for Cyber Defense . . . . . . . . 94Case Study: Using MegaVul to Build an AI-Powered Vulnerability Detector. . 95AI Coding Agents.. . . . . . . . . . . . 103Summary. . . . . . . . . . . . . . 112Test Your Skills. . . . . . . . . . . . . 114Multiple-Choice Questions . . . . . . . . . . . 114Answers to Multiple-Choice Questions. . . . . . . . 116EXERCISES.. . . . . . . . . . . . . . 1175 Introduction to AI-Driven Incident Response.. . . . . . 121Chapter Objectives.. . . . . . . . . . . . 121Foundations of Cybersecurity Incident Response. . . . . . 122Understanding the Traditional Cybersecurity Incident Response Process.. . 123The Functions of Incident Response Teams.. . . . . . . 127The Emergence of Artificial Intelligence in CybersecurityIncident Response. . . . . . . . . . . . 134Summary. . . . . . . . . . . . . . 147Test Your Skills. . . . . . . . . . . . . 148Multiple-Choice Questions.. . . . . . . . . . . 148Answers to Multiple-Choice Questions. . . . . . . . 150Project 5-1: Automated Vulnerability Triage and Patching Workflow.. . . 1516 Real-Time Analysis, Decision-Making, Orchestration, and Automation. .. 153Chapter Objectives . . . . . . . . . . . . 153Real-Time Analysis. . . . . . . . . . . . 154AI-Driven Decision-Making.. . . . . . . . . . . 166The Pitfalls of AI in Security. . . . . . . . . . . 169Orchestration and Automation. . . . . . . . . . 172The Integrated Defense: SOAR and Proactive Resilience in Practice. . . 175Summary. . . . . . . . . . . . . . 180Test Your Skills. . . . . . . . . . . . . 182Answers to Multiple-Choice Questions. . . . . . . . 1857 IoT Security and Cloud Security Using AI.. . . . . . . 187Chapter Objectives.. . . . . . . . . . . . 187Definition of IoT and Cloud Security. . . . . . . . . 188IoT Security Challenges. . . . . . . . . . . 191Vulnerabilities in IoT Devices. . . . . . . . . . 195Case Studies of IoT Security Breaches. . . . . . . . . 197Cloud Security Challenges.. . . . . . . . . . . 200The Application of AI in IoT Security. . . . . . . . . 202The Application of AI in Cloud Security. . . . . . . . 206Limitations of Using Low-Memory AI in IoT and Cloud Security. . . . 208Future Trends in AI-Enhanced IoT and Cloud Security.. . . . . 210Best Practices and Recommendations.. . . . . . . . 213Enhancing IoT and Cloud Security Using Dynamic and Static Malware Analysis.. 215Summary. . . . . . . . . . . . . . 218References. . . . . . . . . . . . . . 218Test Your Skills. . . . . . . . . . . . . 220Multiple-Choice Questions . . . . . . . . . . . 220Answers to Multiple-Choice Questions. . . . . . . . 221EXERCISES AND ANSWERS (Interview Style).. . . . . . . 2228 Advanced Encryption Techniques, Privacy, and Compliance.. . . 225Chapter Objectives.. . . . . . . . . . . . 225AI in Cryptography.. . . . . . . . . . . . 226Enhancing Data Security. . . . . . . . . . . 229Decentralization for Balancing Security with Privacy.. . . . . . 232Privacy-Preserving Techniques.. . . . . . . . . . 235Homomorphic Encryption.. . . . . . . . . . . 237AI and Regulatory Compliance.. . . . . . . . . . 240Advanced Encryption Techniques and the Role of Malware in Encryption. . 248Summary. . . . . . . . . . . . . . 251References. . . . . . . . . . . . . . 251Test Your Skills. . . . . . . . . . . . . 253Multiple-Choice Questions.. . . . . . . . . . . 253Answers to Multiple-Choice Questions. . . . . . . . 254EXERCISES AND ANSWERS (Interview Style) . . . . . . . 2559 Using AI to Enhance Cybersecurity Programs and Policies.. . . 257Chapter Objectives.. . . . . . . . . . . . 257Dynamic Security Policies: Implementation and Adaptation of AI in Security Policies.. . 258AI-Driven Security Adjustments: Real-Time Threat Detection and Response Mechanisms.. . 264Enhancing the Software Development Lifecycle (SDLC): AI Integration in SDLC for Improved Security.. . 268AI-Powered Cybersecurity Governance: Governance Frameworks and Compliance Monitoring.. 270AI-Driven Security Adjustments: Real-Time Threat Detection and Response Mechanisms.. 274AI-Driven Integration of Malware Analysis into Cybersecurity Programs.. . 277Summary. . . . . . . . . . . . . . 280References. . . . . . . . . . . . . . 280Test Your Skills. . . . . . . . . . . . . 281Multiple-Choice Questions.. . . . . . . . . . . 281Answers to Multiple-Choice Questions. . . . . . . . 282EXERCISES AND ANSWERS.. . . . . . . . . . . 28310 Securing AI Implementations.. . . . . . . . . . 285Chapter Objectives.. . . . . . . . . . . . 285The Coalition for Secure AI.. . . . . . . . . . . 286The NIST AI Risk Management Framework.. . . . . . . . 287Threat Modeling AI Systems.. . . . . . . . . . 288Adversarial Machine Learning (AML). . . . . . . . . 318Securing Agentic AI and Multi-Agent Systems (MAS). . . . . . 320Red Teaming in AI Systems.. . . . . . . . . . . 323Continuous Monitoring and Observability.. . . . . . . . 328Summary. . . . . . . . . . . . . . 331Test Your Skills. . . . . . . . . . . . . 332Multiple-Choice Questions.. . . . . . . . . . . 332Answers to Multiple-Choice Questions. . . . . . . . 336Project 10-1: A Playbook for a Hybrid Human-AI Security Team. . . . 3389780135408605, TOC, 12/9/2025