[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8},"actionData","errors","root",{"_28":-5,"_38":-5,"_39":40,"_41":42,"_43":44,"_45":46,"_47":48,"_49":50,"_51":52,"_53":54,"_55":56,"_57":58},"routes/_store+/_readers+/email_author.$slug/route",{"_9":10,"_11":-5},"book",{"_12":13,"_14":15,"_16":17,"_18":19,"_20":21,"_22":23},"currentUser","id","Z2lkOi8vbGVhbnB1Yi9TdG9yZTo6Qm9va1Byb2R1Y3QvNzg3NTk","title","Securing Enterprise AI Agents","state","published","contactAuthorMethod","email","aboutText","

Enterprise AI agents are moving from demos into workflows that touch code, data, tools, tickets, documents, APIs, and customer operations. That changes the security problem. Reviewing prompts, logging chatbot sessions, and writing acceptable-use policies is not enough once an AI system can act.

Securing Enterprise AI Agents shows how to treat agents as operational systems. It covers bounded AI autonomy, AgentSecOps, MCP security, RAG governance, identity controls, approval workflows, audit evidence, evals, observability, incident response, and regulatory readiness as one connected discipline.

The core argument is simple: enterprises do not need maximum autonomy. They need useful agents that can act inside clear boundaries, prove what they did, stay inside policy, and fail safely.

If you are searching for AI agent security, secure AI agents, or practical controls for AI agents in production, this book is the field guide I wish more enterprise teams had before their first agent touched real data, real tools, or real customers.

This book is for CISOs, CIOs, CTOs, enterprise architects, security architects, platform teams, AppSec leaders, AI governance teams, risk owners, auditors, and engineering leaders moving agents from experiments into real enterprise workflows.

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Kick off your book project in 3 hours! Live workshop on Zoom. You’ll leave with a real book project, progress on your first chapter, and a clear plan to keep going. Saturday, June 6, 2026. Learn more…

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