AI Agent Security

A convincing answer is not the same as a safe action.

An AI assistant can produce a perfectly reasonable response while sending the wrong attachment, accessing restricted information, or making an unauthorized change. Once AI moves from generating text to taking action, security becomes a question of authority, execution, and real-world consequences.

AI Agent Security: From Models to Actions explains how to approach that challenge. Through twenty focused chapters, it follows the complete journey of an agent: understanding a request, retrieving information, using an identity, invoking tools, changing business systems, and recovering when something goes wrong.

The book covers prompt injection, retrieval-augmented generation (RAG), persistent memory, identity and authorization, tool execution, sandboxing, the Model Context Protocol (MCP), and multi-agent collaboration. It then connects these technical foundations to red teaming, meaningful security evaluation, runtime monitoring, incident response, and enterprise governance.

Rather than treating safety training, filters, or approval dialogs as complete solutions, the book examines what each control can—and cannot—establish. Workplace scenarios, research findings, and documented incidents help explain why valid credentials do not authorize every action, why a successful API response does not prove a task was completed correctly, and why passing tests does not eliminate uncertainty.

Written for developers, architects, security practitioners, product managers, and technical leaders, this book explains technical concepts where they become useful, without requiring readers to begin with a specialist vocabulary in AI security.

Its central principle is simple: a model may propose an action, but it must not grant itself the authority to act.

The goal is not to make AI infallible. It is to build systems that constrain its authority, verify its actions, contain failures, and support accountable human oversight.