Author：Dan Borges and David Campbell

AI systems have moved beyond generating text into taking action. They're in production. They query internal data, make API calls, and interact with other production systems, often with more access than most humans get. AI systems aren't deterministic; they reason, adapt, and operate on untrusted input in ways that traditional security models simply weren't designed for. This creates new vulnerabilities and shifts the entire control surface. This book is about that shift. In AI Security Engineering, Dan Borges and David Campbell show you how to rethink security for AI systems built on retrieval pipelines, persistent memories, and agents that take action. Drawing from real-world adversarial testing and production deployments, they focus on how these systems actually fail: prompt injection that turns inputs into instructions, poisoned retrieval that corrupts decisions at runtime, and agents that quietly accumulate more authority than intended. Rather than relying on the model to do the right thing you'll learn how to design systems that constrain what AI systems are allowed to do, enforce least privilege at the capability level, and build architecture that can observe, interrupt, and contain failures when they happen.