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Secure enterprise AI agents with bounded autonomy, AgentSecOps, MCP security, RAG governance, identity controls, evals, policy, observability, audit evidence, and production-ready security patterns.Key FeaturesBuild secure AI agents with bounded autonomy and layered controlsSecure MCP, RAG, identity, tool calls, and agent permissionsApply AgentSecOps, evals, observability, policy, and incident responseBook DescriptionAI 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.What you will learnClassify AI agents by authority, risk, and blast radiusDesign secure seven-layer architectures for enterprise agentsSecure MCP servers, tool calls, and agent capability boundariesApply delegated identity, least privilege, and approval controlsProtect RAG pipelines from poisoning, leakage, and unsafe retrievalBuild evals, observability, and regression gates for agentsEnforce agent policies as code and prepare incident responseImplement continuous AgentSecOps and production readiness checksWho this book is forThis book is for engineers moving AI agents into production, enterprise and security architects, and security, risk, audit and governance teams assessing agent deployments. AI platform leaders and technical decision-makers in regulated or high-impact environments will also benefit. Readers should understand application architecture APIs, cloud services, identity and access management, and basic LLM concepts. No particular agent framework is required.
Secure enterprise AI agents with bounded autonomy, AgentSecOps, MCP security, RAG governance, identity controls, evals, policy, observability, audit evidence, and production-ready security patterns.Key FeaturesBuild secure AI agents with bounded autonomy and layered controlsSecure MCP, RAG, identity, tool calls, and agent permissionsApply AgentSecOps, evals, observability, policy, and incident responseBook DescriptionAI 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.What you will learnClassify AI agents by authority, risk, and blast radiusDesign secure seven-layer architectures for enterprise agentsSecure MCP servers, tool calls, and agent capability boundariesApply delegated identity, least privilege, and approval controlsProtect RAG pipelines from poisoning, leakage, and unsafe retrievalBuild evals, observability, and regression gates for agentsEnforce agent policies as code and prepare incident responseImplement continuous AgentSecOps and production readiness checksWho this book is forThis book is for engineers moving AI agents into production, enterprise and security architects, and security, risk, audit and governance teams assessing agent deployments. AI platform leaders and technical decision-makers in regulated or high-impact environments will also benefit. Readers should understand application architecture APIs, cloud services, identity and access management, and basic LLM concepts. No particular agent framework is required.
