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 Features:
- Build secure AI agents with bounded autonomy and layered controls
- Secure MCP, RAG, identity, tool calls, and agent permissions
- Apply AgentSecOps, evals, observability, policy, and incident response
Book Description:
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.
What You Will Learn:
- Classify AI agents by authority, risk, and blast radius
- Design secure seven-layer architectures for enterprise agents
- Secure MCP servers, tool calls, and agent capability boundaries
- Apply delegated identity, least privilege, and approval controls
- Protect RAG pipelines from poisoning, leakage, and unsafe retrieval
- Build evals, observability, and regression gates for agents
- Enforce agent policies as code and prepare incident response
- Implement continuous AgentSecOps and production readiness checks
Who this book is for:
This 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.
Table of Contents
- From chatbots to actors
- The agentic failure modes
- The enterprise agent operating model
- Reference architecture for secure agents
- MCP and the tool-using AI stack
- Identity for agents
- Securing tool calls
- Evals are the new test suite
- Observability for agents
- Agent reliability engineering
- RAG as an attack surface
- Production RAG architecture
- Knowledge governance
- Coding agents in professional teams
- Security boundaries for AI coding
- From vibe coding to governed delivery
- Agent deployment patterns
- Policy as code for AI agents
- Incident response for AI systems
- Continuous AgentSecOps: build-time and run-time security as one architecture
- The production readiness checklist
