Overview

Architecting Secure LLM Systems: Threat Modeling, Trust Boundaries, and Defense-in-Depth for Production AIAs organizations increasingly deploy large language models (LLMs) in real-world applications, the risks of data leaks, prompt injections, and operational failures grow exponentially. This book is your definitive guide to building AI systems that are not only powerful but inherently secure. It's designed for engineers, security leaders, and AI product teams who need more than surface level guidance, they need a practical, end-to-end framework to protect production AI systems. Inside, you will discover how to treat LLM security as a full lifecycle discipline. From mapping trust boundaries to modeling threats, and from implementing defense-in-depth strategies to designing secure agentic workflows, every concept is explained with clear, real-world examples. You'll gain actionable insights into the latest standards and frameworks, including OWASP, NIST, and MITRE, so that your team can align production practices with industry-leading guidance. You will learn how to: Identify and mitigate vulnerabilities before they become critical breaches. Build secure architectures for RAG pipelines, memory-enabled LLMs, and tool-integrated workflows. Detect, respond to, and prevent prompt injection attacks and data exfiltration. Implement layered controls, sandboxing, and runtime policies that keep your AI system resilient under pressure. Enable cross-functional collaboration between AI developers, security engineers, and leadership to embed security into the very DNA of your AI products. By the end of this book, you will not only understand the threats facing modern LLM systems, you will have the tools, methods, and confidence to engineer production-ready AI that is both trustworthy and resilient. For anyone tasked with deploying LLMs safely, this is not just a guide, it's the essential manual that ensures your systems operate securely, your data remains protected, and your AI innovations can thrive without compromise. Secure, resilient, and production-ready AI is possible and it starts here.

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