AI-Driven Security: Enhancing Large Language Models and Cybersecurity : Large Language Models (LLMs) Security

Kiran Kumar Patibandla

ISBN 10: 6208434718 / ISBN 13: 9786208434717

Published by LAP LAMBERT Academic Publishing, 2025

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - In an era of rapid advancements in artificial intelligence, cybersecurity threats continue to evolve, particularly in the domain of large language models, financial systems, and password security. This book delves into the critical aspects of AI-driven security, offering insights into risk management, defensive mechanisms, and ethical AI deployment. Through a structured approach, this book explores:1.The vulnerabilities of financial large language models and prompt injection attack defenses.2.Ownership protection mechanisms through watermarking and fingerprinting techniques.3.AI-powered security policy automation for operating systems.4.Password security analysis leveraging hybrid rule-based and ML techniques. With extensive research and case studies, 'AI-Driven Security' provides a comprehensive guide to fortifying AI applications against cyber threats, making it a must-read for cybersecurity professionals, AI researchers, and industry practitioners.

Seller Inventory # 9786208434717

Bibliographic Details

• Title: AI-Driven Security: Enhancing Large Language Models and Cybersecurity : Large Language Models (LLMs) Security
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2025
• Language: English
• ISBN 10: 6208434718
• ISBN 13: 9786208434717
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 10D millimeters 220 millimeters width by 150 millimeters height by 10 millimeters depth
• Weight: 233 grams

About this title

Synopsis

In an era of rapid advancements in artificial intelligence, cybersecurity threats continue to evolve, particularly in the domain of large language models, financial systems, and password security. This book delves into the critical aspects of AI-driven security, offering insights into risk management, defensive mechanisms, and ethical AI deployment. Through a structured approach, this book explores:1.The vulnerabilities of financial large language models and prompt injection attack defenses.2.Ownership protection mechanisms through watermarking and fingerprinting techniques.3.AI-powered security policy automation for operating systems.4.Password security analysis leveraging hybrid rule-based and ML techniques. With extensive research and case studies, "AI-Driven Security" provides a comprehensive guide to fortifying AI applications against cyber threats, making it a must-read for cybersecurity professionals, AI researchers, and industry practitioners.