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COMBATING CYBERATTACKS TARGETING THE AI ECOSYSTEM: Assessing Threats, Risks, and Vulnerabilities
This book explores in detail the AI-driven cyber threat landscape, including inherent AI threats and risks that exist in Large Language Models (LLMs), Generative AI applications, and the AI infrastructure. The book highlights hands-on technical approaches to detect security flaws in AI systems and applications utilizing the intelligence gathered from real-world case studies. Lastly, the book presents a very detailed discussion of the defense mechanisms and practical solutions to secure LLMs, GenAI applications, and the AI infrastructure. The chapters are structured with a granular framework, starting with AI concepts, followed by practical assessment techniques based on real-world intelligence, and concluding with required security defenses. Artificial Intelligence (AI) and cybersecurity are deeply intertwined and increasingly essential to modern digital defense strategies. The book is a comprehensive resource for IT professionals, business leaders, and cybersecurity experts for understanding and defending against AI-driven cyberattacks.
FEATURESIncludes real-world case studies with detailed examples of AI-centric attacks and defense mechanismsFeatures hands-on security assessments with practical techniques for evaluating the security of AI systemsDemonstrates advanced defense strategies with proven methods to protect LLMs, GenAI applications, and the infrastructureTABLE OF CONTENTS
1: Introduction to LLMs, GenAI Applications and the AI Infrastructure. 2: The AI Trust, Compliance, and Security. 3: AI Threat Dissecting the Risks and Attack Vectors. 4: Threats and Attacks Targeting the AI Real-world Case Studies. 5: Security Assessment of LLMs, GenAI Applications, and the AI Infrastructure. 6: Defending LLMs, GenAI Applications, and the AI Infrastructure Against Cyberattacks. Machine Learning / AI terms. Index.
ABOUT THE AUTHOR
Aditya K. Sood (PhD) is a cybersecurity practitioner with more than 16 years of experience working with cross-functional teams, management, and customers to create the best-of-breed information security experience. His articles have appeared in magazines and journals, including IEEE, Elsevier, ISACA, Virus Bulletin, and USENIX, and he is the author of Empirical Cloud Security 2/E (Mercury Learning) and Targeted Cyber Attacks (Syngress). He has presented his research at industry leading security conferences such as Black Hat, RSA, APWG, DEFCON, Virus Bulletin, and others.
FEATURESIncludes real-world case studies with detailed examples of AI-centric attacks and defense mechanismsFeatures hands-on security assessments with practical techniques for evaluating the security of AI systemsDemonstrates advanced defense strategies with proven methods to protect LLMs, GenAI applications, and the infrastructureTABLE OF CONTENTS
1: Introduction to LLMs, GenAI Applications and the AI Infrastructure. 2: The AI Trust, Compliance, and Security. 3: AI Threat Dissecting the Risks and Attack Vectors. 4: Threats and Attacks Targeting the AI Real-world Case Studies. 5: Security Assessment of LLMs, GenAI Applications, and the AI Infrastructure. 6: Defending LLMs, GenAI Applications, and the AI Infrastructure Against Cyberattacks. Machine Learning / AI terms. Index.
ABOUT THE AUTHOR
Aditya K. Sood (PhD) is a cybersecurity practitioner with more than 16 years of experience working with cross-functional teams, management, and customers to create the best-of-breed information security experience. His articles have appeared in magazines and journals, including IEEE, Elsevier, ISACA, Virus Bulletin, and USENIX, and he is the author of Empirical Cloud Security 2/E (Mercury Learning) and Targeted Cyber Attacks (Syngress). He has presented his research at industry leading security conferences such as Black Hat, RSA, APWG, DEFCON, Virus Bulletin, and others.
Kindle Edition
Published October 8, 2024
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