About this book:

*Red Teaming with Generative AI* provides a comprehensive technical and strategic framework for security professionals to use large language models (LLMs) and multimodal AI as force multipliers in offensive simulations. The book argues that as adversaries adopt AI to industrialize phishing, automate reconnaissance, and develop polymorphic malware, red teams must embrace these same tools to pressure-test defenses. By moving from "artisanal" manual testing to "industrial-scale" automated simulations, organizations can achieve greater coverage across the MITRE ATT&CK framework and better prepare blue teams for the increased velocity of modern threats.

The text details practical execution strategies, including advanced prompt engineering (such as few-shot learning and role-playing), the creation of synthetic datasets for training detection models, and the orchestration of autonomous agents within isolated sandbox environments. It specifically covers the simulation of multi-channel social engineering—spanning email, SMS, chat, and deepfake voice—while emphasizing the need for "Human-in-the-Loop" (HITL) oversight to manage model hallucinations and ensure technical fidelity. Furthermore, it introduces "AI safety testing" to probe the vulnerabilities of the AI models themselves, such as prompt injection, data poisoning, and model inversion.

Central to the book is the shift from a siloed adversarial approach to a collaborative "Purple Teaming" model. By integrating red team outputs directly into detection engineering and incident response exercises, organizations can dramatically reduce Mean Time to Detect (MTTD) and Mean Time to Respond (MTTR). The author provides specific metrics for success, moving beyond simple vulnerability counts to measure defensive efficacy, variant coverage, and tangible risk reduction. This data-driven approach allows security leadership to justify investments and demonstrate a measurable increase in organizational resilience.

Finally, the book addresses the critical non-technical pillars of a mature AI red teaming program: ethics, law, and governance. It outlines the necessity of an AI Governance Board to manage legal compliance (such as GDPR), intellectual property risks, and the psychological safety of employees involved in realistic simulations. Looking toward the future, the text anticipates a landscape dominated by autonomous AI agents and evolving global regulations, urging security practitioners to view generative AI not merely as a tool for content generation, but as a strategic substrate for building adaptive, "proactive resilience."