AI agents can read sensitive data, call tools and APIs, write code, change system state, and act on behalf of users. Each action needs explicit authority and enforceable limits.




Securing Enterprise AI Agents is a practical field guide to bounded AI autonomy. Learn to classify agent risk, assign ownership and approval paths, and design a seven-layer reference architecture. Secure Model Context Protocol (MCP) servers and tool calls, apply delegated identity and least privilege, and govern retrieval augmented generation (RAG) and enterprise knowledge.




The book connects evaluations, observability and reliability controls with secure coding-agent workflows, policy as code, incident response and continuous AgentSecOps. Worked enterprise scenarios, exercises, illustrative code and a production-readiness checklist help engineering, security, risk and audit teams turn the guidance into controls they can assess.




By the end, you will be able to define boundaries for agent actions, collect audit evidence, plan for failures and assess whether the controls justify production deployment.