Guide to AI for Cybersecurity

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

         Includes complete instructor resources for flexible course adoption PowerPoint slides, laboratory exercises, assessment questions, and implementation projects

         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.

Muth