AI-Powered Digital Cyber Resilience
Published on 6. January 2026
400 pages
978-0-13-540862-9 (ISBN)
Description
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.
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.
More details
Language
English
Place of publication
Boston
United States
Publishing group
Pearson Education (US)
Target group
Professional and scholarly
File size
18,60 MB
ISBN-13
978-0-13-540862-9 (9780135408629)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Omar Santos | Petar Radanliev
AI-Powered Digital Cyber Resilience
Book
03/2026
Addison Wesley
€37.50
Shipment within 15-20 days
Persons
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.
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.
Content
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
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
