Large Language Models (LLMs) are rapidly transforming how enterprises operate, powering customer support, internal assistants, automated workflows, search, analytics, and decision-making systems. But as organizations adopt AI at scale, they are also introducing a new and expanding attack surface. Jailbreaking LLMs explores how attackers manipulate AI systems through prompt injection, jailbreaks, adversarial inputs, data poisoning, context manipulation, retrieval attacks, and unsafe tool usage to bypass safeguards, leak sensitive data, and influence AI behavior in unexpected ways.

This book provides a practical guide to understanding, testing, and defending enterprise AI systems in the real world. Through real attack scenarios, security frameworks, red-teaming methodologies, governance strategies, and defensive architecture patterns, readers will learn how to build secure, resilient, and enterprise-ready LLM deployments. Covering everything from RAG security and agentic systems to incident response, AI governance, runtime monitoring, and future attack trends, this book connects AI innovation with modern cybersecurity practices.

What you will learn

Understand how LLM jailbreaks, prompt injection, and adversarial attacks work

Identify vulnerabilities across enterprise AI systems, RAG pipelines, agents, and APIs

Design and deploy secure, enterprise-ready LLM architectures

 Implement monitoring, logging, detection, and incident response workflows for AI systems

Apply red-teaming and defensive testing strategies to evaluate LLM security

Build governance, compliance, and ethical AI controls into enterprise deployments

Understand emerging AI attack trends and future cybersecurity risks