Guide to AI for Cybersecurity: Principles, Frameworks, and Practical Implementation (Texts in Computer Science) - Tapa dura

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Professional Background

• 30+ years of experience in cybersecurity consulting and training
• Developer and delivery lead for comprehensive cybersecurity training programs
• Expertise in NIST, NCSC, and ISO 27001 framework implementations
• Practical experience in AI security tool deployment and assessment

• M.Sc; M.Tech in Computer Science and PhD in Software Engineering
• Professional Recognitions: 20 plus years as an Associate Professor at Leeds Beckett University, Leeds, UK. Fellow of BCS, Fellow of IoA (Institute of Analytics), Senior member of IEEE, and ACM. Senior Fellow of HEA (Higher Education Authority, UK)
• Published author with Springer (Blockchain Engineering and Engineering AI Ethics by Design), Software Engineering in the Era of Cloud Computing, 2020) and Elsevier (Ethics of Blockchain)
• Over 100s of Journals and 1000s of Conference and keynote speaker globally.

De la contraportada

With cybercrime costs exceeding $10.5 trillion annually and ransomware attacks predicted every two seconds by 2031, traditional signature-based security has reached critical breaking points. Guide to AI for Cybersecurity provides the essential roadmap for harnessing artificial intelligence as a force multiplier against sophisticated, AI-powered threats.

This comprehensive textbook bridges the gap between artificial intelligence theory and practical cybersecurity applications through 18 chapters organized around an innovative detection, response, prediction, and prevention (DRPP) framework. Drawing from recent high-impact incidents--including the 2025 Collins Aerospace cyberattack, the Marks & Spencer ransomware attack, and the Co-op data breach --readers progress from foundational concepts to advanced implementations, gaining hands-on experience with production-ready code examples, real-world case studies, and comprehensive deployment guidance for AI-powered security solutions.

Topics and features:

- Introduces the DRPP framework for systematically implementing AI security across the complete security lifecycle

- Provides comprehensive coverage of machine learning (ML) for threat detection, adversarial AI defenses, and automated incident response

- Integrates ethics, governance, and regulatory compliance (GDPR, CCPA, AI Act) throughout, with dedicated coverage of privacy-preserving techniques

- Offers detailed guidance on integrating AI capabilities with industry standards while maintaining compliance requirements

This essential textbook/guide provides comprehensive coverage suitable for graduate students in computer science, cybersecurity, or AI/ML programs, as well as cybersecurity professionals seeking to master AI-powered defense techniques. Software architects building secure AI systems, academic instructors developing AI security courses, and researchers investigating adversarial machine learning also will find the volume invaluable.

Muthu Ramachandran is Research Consultant at Forti5 Technologies Ltd, UK, and Visiting Professor Extraordinarius at University of South Africa.