AI applications are useful, but they can also become unsafe in subtle ways.
A normal application already needs authentication, authorization, input validation, logging, and careful handling of secrets. AI applications add another layer of risk: prompts can be manipulated, retrieved documents can overshare, tools can be exposed too broadly, and generated outputs can look safe while breaking policy.
Securing AI Applications is a hands-on guide to building safer AI workflows.
Instead of treating AI security as abstract theory, this book follows a small companion project: ai_security_lab, a fictional support-assistant application with support tickets, customer records, policy documents, uploaded content, sensitive tool actions, and audit logs. The project starts with weak boundaries and hardens them step by step.
Inside, you will learn how to:
- recognize prompt injection and untrusted input
- treat retrieved documents as data, not instructions
- reduce sensitive data before it reaches the AI workflow
- design safer tool contracts and permission checks
- add human approval gates for higher-risk actions
- validate structured outputs before using them downstream
- use audit logs to make AI actions reviewable
- test security controls instead of leaving them as advice
- prepare a practical deployment checklist for AI features
This book is not about securing a model in isolation. It is about securing the workflow around the model: prompts, retrieval, tools, outputs, approvals, logs, and boundaries.
If you are building AI features into real applications, this guide will help you think clearly about what can go wrong - and how to make those risks visible, testable, and easier to control.
