Protecting the Future of Enterprise Security
Buch, Englisch, 737 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 1130 g
ISBN: 979-8-8688-2957-4
Verlag: Apress
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
Who this book is for
This book is for cybersecurity professionals, AI/ML engineers, enterprise architects, security analysts, SOC teams, IT leaders, and technical decision-makers responsible for building, deploying, or securing AI-powered systems. It is also valuable for practitioners who want to better understand the security, governance, and operational challenges that come with adopting Large Language Models in enterprise environments.
Zielgruppe
Professional/practitioner
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
Chapter 1. Jailbreaking LLMs and Its Security Implications.- Chapter 2. LLM Security Infrastructure.- Chapter 3: Designing and Deploying LLMs for Enterprise Use.- Chapter 4. Security Vulnerabilities and Design Flaws in LLMs.- Chapter 5. Exploitation of Security Vulnerabilities in LLMs.- Chapter 6: Strategies for Safeguarding Jailbreak Attempts.- Chapter 7. The Role of Red Teaming in LLM Security.- Chapter 8. Responding to Jailbreaking in Enterprise LLMs.- Chapter 9. Ethics and AI Governance in Enterprise Security.- Chapter 10. LLMs and the Future of Cybersecurity.- Optional Appendix A: World of LLMs.- Optional Appendix B: Architecture of LLMs.
