Generative AI Security

Securing generative AI systems presents challenges that don’t map neatly onto traditional cybersecurity frameworks — the attack surfaces are different, the failure modes are different, and the rapid evolution of the technology outpaces most existing security standards and practices. Generative AI Security takes both the theoretical and practical dimensions of this challenge seriously. The five authors bring complementary expertise across AI research, enterprise security, and risk governance, which gives the book an unusually broad perspective: it doesn’t reduce the problem to purely technical controls or purely policy considerations but treats it as the genuinely multi-layered challenge it is. The book covers threat modeling for generative AI systems, privacy risks, model integrity, deployment security, and the governance frameworks organizations need to manage these risks at scale. As part of Springer’s Future of Business and Finance series, it carries an academic rigor that distinguishes it from more practitioner-oriented security guides, making it particularly well-suited to readers who want a thorough conceptual foundation alongside practical guidance. For security architects, AI engineers, and risk professionals working with generative AI systems, this offers a more complete and rigorous treatment of the security landscape than most resources in this fast-moving space currently provide.

Author:
Ken Huang, Yang Wang, Ben Goertzel, Yale Li, Sean Wright