Book Publishes Practical Guide to Securing AI Systems

Security and governance checklists that map directly to deployed generative-AI patterns remain useful to practitioners; playbooks that combine threat examples, mitigation steps, and governance controls reduce time-to-remediation when models are integrated with production data and agentic tooling.

Industry-pattern observations indicate teams implementing production generative systems typically need runnable mitigations for prompt injection, monitoring approaches for RAG theft or poisoning, and governance checklists that tie technical controls to audit evidence. Editorial analysis: A single-volume, operationally focused guide that bundles examples across RAG, agents, and Zero Trust can shorten onboarding for security and ML teams, provided the material includes concrete detection signatures, test cases, and deployment checklists. Observers should verify author credentials and sample chapters before adopting operational controls wholesale, since implementation details and threat models vary by architecture and data sensitivity.