Combating Cyberattacks Targeting the AI Ecosystem
Assessing Threats, Risks, and Vulnerabilities
- Publisher
Mercury Learning and Information - ISBN 9781501523243
- Language English
- Pages 234 pp.
- Size 7" x 9"
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- Publisher
Mercury Learning and Information - ISBN 9781501520549
- Language English
- Pages 234 pp.
- Size 7" x 9"
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- Publisher
Mercury Learning and Information - ISBN 9781501520556
- Language English
- Pages 234 pp.
- Size 7" x 9"
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.
FEATURES:
- Includes real-world case studies with detailed examples of AI-centric attacks and defense mechanisms
- Features hands-on security assessments with practical techniques for evaluating the security of AI systems
- Demonstrates advanced defense strategies with proven methods to protect LLMs, GenAI applications, and the infrastructure
1: Introduction to AI: LLMs, GenAI Applications and the AI Infrastructure
2: The AI Trust, Compliance, and Security
3: AI Threat Landscape: Dissecting the Risks and Attack Vectors
4: Threats and Attacks Targeting the AI Ecosystem: 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
Appendix: Machine Learning / AI terms
Index
Aditya K. Sood, PhD
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. 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.