Overview

Generative AI for Cybersecurity and Privacy offers a groundbreaking exploration of how generative artificial intelligence is reshaping the landscape of cybersecurity and privacy protection in an era of rapid digital transformation. As cyber threats grow in sophistication and scale, this book provides a timely and authoritative guide to harnessing generative AI to safeguard digital ecosystems, secure sensitive data, and address emerging challenges across diverse domains. Spanning a series of expertly curated chapters, this volume delves into cutting-edge advancements and practical applications of generative AI in cybersecurity. It covers critical areas such as AI-driven threat detection and response, privacy-preserving AI models, secure IoT and cloud computing frameworks, and robust defenses for cyber-physical systems, including Smart Cities and wireless networks. The book balances rigorous theoretical foundations with real-world case studies, making it an essential resource for researchers, security professionals, policymakers, and organizational leaders. The book offers comprehensive coverage of key topics, including: • Leveraging generative AI for proactive threat detection, risk analysis, and automated incident response • Innovative approaches to data privacy, compliance, and governance in AI-driven systems • Advanced methodologies for securing IoT, mobile applications, and cloud infrastructures • Practical frameworks for integrating generative AI into cybersecurity strategies for critical infrastructures • Emerging applications of generative AI in personalized, secure digital experiences, such as e-commerce and smart systems Authored by a global team of leading researchers and practitioners, this book stands out by not only addressing current cybersecurity and privacy challenges but also proposing forward-thinking, scalable solutions powered by generative AI. Unlike traditional resources, it emphasizes the transformative potential of AI in revolutionizing risk analysis, threat mitigation, and privacy preservation across multiple domains. Whether you’re navigating the complexities of IoT, cloud security, or emerging cyber threats, Generative AI for Cybersecurity and Privacy equips you with the knowledge and tools to build intelligent, secure, and future-ready strategies for a dynamic digital world.

Full Product Details

Author:   Youssef Baddi ,  Yassine Maleh (National School of Applied Sciences) ,  Izzat Alsmadi ,  Mohamed Lahby
Publisher:   Taylor & Francis Ltd
Imprint:   CRC Press
Weight:   0.720kg
ISBN:  

9781032980201

ISBN 10:   1032980206
Pages:   286
Publication Date:   25 November 2025
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   Not yet available  
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Table of Contents

Preface Aknowledgments Part I: Introduction to Generative AI for Cybersecurity and Privacy 1. Understanding Generative AI: Concepts and Frameworks o Overview of Generative AI o Historical Context and Evolution o Key Algorithms and Models 2. The Importance of Generative AI in Cybersecurity and Privacy o Impact on Cybersecurity o Enhancing Privacy with AI o Ethical and Legal Considerations Part II: Applications of Generative AI in Cybersecurity 3. Anomaly Detection with Generative AI o Techniques and Approaches o Real-world Applications and Case Studies o Performance Metrics and Evaluation 4. Generative Adversarial Networks (GANs) for Cyber Threat Intelligence o Generating Threat Signatures o Predictive Analytics for Threat Forecasting o Practical Implementations 5. Generative AI for Malware Detection and Analysis o Approaches to Malware Classification o Behavioral Analysis using Generative Models o Advanced Threat Detection Mechanisms 6. Phishing Detection and Prevention with Generative AI o Identifying and Mitigating Phishing Attacks o Simulating Phishing Scenarios with Generative Models o Best Practices and Countermeasures Part III: Enhancing Privacy and Data Security with Generative AI 7. Privacy-preserving Generative Models o Differential Privacy Techniques o Data Anonymization and Secure Multi-party Computation o Case Studies and Applications 8. Secure Data Sharing and Federated Learning o Ensuring Data Security in Collaborative Environments o Blockchain for Secure Data Transactions o Practical Implementations and Challenges 9. AI-driven Encryption and Decryption o Generative Approaches to Cryptography o Enhancing Existing Security Protocols o Emerging Trends and Innovations Part IV: Case Studies and Real-world Implementations 10. Generative AI in Financial Sector Security o Threat Detection and Fraud Prevention o Addressing Privacy Concerns o Lessons Learned and Best Practices 11. Generative AI in Healthcare Security o Protecting Patient Data o Detecting and Mitigating Security Breaches o Future Directions and Research Opportunities 12. Generative AI in Government and Defense o Enhancing National Security with AI o Cyber Warfare and AI-driven Defense Mechanisms o Policy Implications and Ethical Considerations Part V: Managing Generative AI in Cybersecurity and Privacy 13. Developing a Generative AI Strategy for Cybersecurity o Planning, Prioritization, and Resourcing o Implementation Strategies o Risk Management Approaches 14. Incident Response and Generative AI o Preparing for AI-driven Cybersecurity Incidents o Response Strategies and Best Practices o Post-incident Analysis and Improvement 15. Business Continuity and Disaster Recovery with Generative AI o Ensuring Resilience in the Face of Cybersecurity Threats o AI-driven Continuity Planning o Recovery Strategies 16. Measuring and Reporting on AI-enhanced Cybersecurity o Metrics, Dashboards, and Communication o Evaluating AI Effectiveness o Reporting to Stakeholders About the Authors Index

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Author Information

The authors of this book hold Ph.D degrees, and they have published many journal articles and book chapters. Dr.Yassine Maleh is the author of many published books with well-known publishers like CRC Press and Springer.

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