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Generative AI Security | Ken Huang, Wang Yang, Ben Goertzel, Yale Li, Sean Wright & Jyoti Ponnapalli
Generative AI Security | Ken Huang, Wang Yang, Ben Goertzel, Yale Li, Sean Wright & Jyoti Ponnapalli
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Generative AI Security: Theories and Practices
Extended Synopsis
This authoritative reference volume explores the revolutionary intersection of Generative AI (GenAI) and cybersecurity. Presenting a comprehensive guide that seamlessly intertwines advanced theories with real-world technical practices, this text equips cybersecurity professionals, CISOs, AI researchers, developers, and architects with a definitive understanding of GenAI's profound impacts on enterprise security architecture.
The scope of the work ranges from the theoretical foundations of GenAI—including its underlying principles, advanced neural architectures, and cutting-edge research paradigms—to specific implementations of data security, model security, application-level security, and the emerging operational structures of LLMOps and DevSecOps. Crucially, the text addresses complex global AI regulatory environments, ethical considerations, evolving threat landscapes, and privacy-preserving strategies, offering actionable insights and hands-on frameworks required to build a resilient GenAI security program.
Editor Biographies & Contributors
This volume is compiled and edited by a distinguished panel of global tech leaders and researchers. Lead editors include Ken Huang, Yang Wang, renowned artificial general intelligence pioneer Ben Goertzel, Yale Li, Sean Wright, and Jyoti Ponnapalli. Together, they bridge the gap between academic research, regulatory compliance, and enterprise cloud architecture to establish rigorous foundational practices for securing intelligent systems.
Reader Targeting
- Chief Information Security Officers (CISOs) and enterprise risk managers.
- AI engineers, developers, and software architects designing LLM applications.
- Cybersecurity analysts and researchers investigating adversarial AI and model vulnerabilities.
- Graduate and advanced undergraduate students in Computer Science, Data Security, and Technology Policy programs.
Accolades & Academic Overview
- Published as part of the prestigious Springer "Future of Business and Finance" series (Volume 81).
- Recognized as an essential, rigorously peer-reviewed framework for understanding global AI compliance and structural risk mitigation.
Bibliographic & Physical Specifications
| Publisher | Springer Cham (Imprint: Springer) |
|---|---|
| Series | Future of Business and Finance (Book 81) |
| Publication Date | April 05, 2024 |
| Edition Details | 2024th Edition / First Printing (Stated) |
| Format & Binding | Hardcover (Printed boards over rigid binder board; issued without dust jacket) |
| ISBN-13 / ISBN-10 | 9783031542510 / 3031542517 |
| Page Count | 373 (Includes xxxv, 338 pages of investigative modules, prompt matrices, and global regulatory indices) |
| Illustrations | Yes (Features color structural diagrams, data-flow models, threat vectors, and cybersecurity charts) |
| Dimensions & Weight | 9.30 x 6.10 x 0.94 inches | 23.8 oz (675 grams) |
| BISAC Categories | COMPUTERS / Security / General COMPUTERS / Artificial Intelligence / General BUSINESS & ECONOMICS / Insurance / Risk Assessment & Management |
| BIC / Thema Classifications | UL (Computer security) UB (Information technology: general issues) |
| LC Classification | HF54.5-54.56 |
| Dewey Decimal | 005.8 |
Frequently Asked Questions
Does this text address deployment frameworks like LLMOps and DevSecOps?
Yes. The volume features targeted chapters that dive directly into the operationalization of secure generative models, providing structural approaches to data security pipelines and infrastructure vulnerabilities.
How thoroughly are international legal requirements analyzed?
The book includes extensive reviews of emerging AI regulations around the globe, outlining compliance demands and privacy-preservation frameworks necessary for multinational enterprise operations.
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