Caroline Wong Publishes AI Cybersecurity Handbook Guide

According to Wiley's catalog and the O'Reilly online listing, The AI Cybersecurity Handbook was released in March 2026, authored by Caroline Wong and published by Wiley. O'Reilly and publisher metadata list the book at 272 pages; the ISBN appearing in publisher and reseller records is 978-1394340866. Retail listings on Amazon, Target, and OverDrive describe the title as aimed at cybersecurity professionals, IT managers, developers, and business leaders and characterise its level as introductory to intermediate.

Per O'Reilly's table-of-contents extract and the OverDrive synopsis, the book surveys both offensive and defensive uses of AI, with chapter-level coverage including: AI-enhanced reconnaissance; the evolution from scripts to self-learning and autonomous malware; bot engineering; language- and tone-driven social engineering and deepfakes; rethinking vulnerability scoring and probabilistic decision-making; and AI-enabled incident response and post-mortem analysis. OverDrive and publisher material also highlight sections on explainability, ethics, and operational dependencies such as data quality.

Industry-pattern observations: the chapter topics mirror current practitioner concerns where AI is both a force multiplier for attackers and a force multiplier for defenders. Documentation on reconnaissance, generative deepfakes, and autonomous malware aligns with public reporting of AI accelerating attack automation and personalization. Conversely, coverage of adaptive detection, probabilistic scoring, and local model approaches echoes ongoing practitioner efforts to move beyond static, rules-based detection toward contextual, data-driven models.

For practitioners: this book assembles a broad cross-section of operational topics that teams face when integrating AI into security tooling and incident response. The emphasis on explainability, data dependencies, and operationalization addresses recurring frictions reported in vendor and academic discourse about model reliability, false positives, and governance. While the book is not a primary research paper or a new tooling release, it compiles applied guidance and case patterns that may help security teams evaluate where to pilot AI capabilities.

For observers and security teams: adoption signals include vendor feature releases for adaptive incident response, published incident post-mortems demonstrating AI-derived detection improvements, and industry guidance on explainability and model governance from standards bodies or regulators. Reporting and vendor documentation that specifically cite operational metrics for AI-driven detection (for example, measured reduction in mean-time-to-detect or false positive rates) will be the most useful follow-ups to the book's practical claims.

The book aggregates contemporary operational thinking on AI in security and frames technical and governance tradeoffs practitioners will encounter when shifting to AI-augmented defenses. It is positioned as a practical handbook rather than a research monograph, and publisher and retailer listings emphasise hands-on strategies and implementation considerations.