Blue Teaming for LLM Security: Defending LLM Environments, Prompt Filtering with Detection Rules, Incident Response, and MLSecOps Playbooks

Blue Teaming for LLM Security: Defending LLM Environments, Prompt Filtering with Detection Rules, Incident Response, and MLSecOps Playbooks

In a world where generative AI is rapidly reshaping everything—from internal chatbots to customer-facing copilots— LLM abuse, prompt injection, and AI data leaks are no longer theoretical threats. Blue Teaming for LLM Security is the first hands-on, defensive guide built exclusively for cybersecurity professionals, engineers, and SOC teams tasked with protecting Large Language Model (LLM) environments. Written by seasoned AI security specialist Sloan S. Benson , this book arms readers with the real-world playbooks, tools, and detection strategies needed to defend against modern AI threats. From prompt filtering and token tracing to incident response workflows , attack surface monitoring , and MLSecOps integrations , every chapter is packed with actionable techniques and complete, working examples —no fluff, no hype. What Makes This Book a Must-Have: Covers cutting-edge topics like jailbreak detection, role-based prompt control, token flow logging, and LLM observability. - Builds real-world defenses using SIEMs, OpenTelemetry, LangSmith, and Prometheus/Grafana —tools that modern blue teams already use. - Maps practices to NIST AI RMF, ISO 42001, and GDPR , helping you meet compliance and governance requirements in LLM pipelines. - Includes realistic attack scenarios and incident response patterns you can actually deploy. Whether you're defending internal AI assistants, foundation models, or custom GPT agents , this book delivers the clarity, depth, and credibility you need to stay ahead. Concise yet complete , it’s your go-to reference for secure AI development , LLM threat detection , and automated blue team workflows . If you work in cloud security , DevSecOps , MLOps , or a modern SOC , this is the LLM defense guide you’ve been waiting for.