Generative AI Security: Protecting Users from Impersonation and Privacy Breaches

Abstract

This study examines the evolving landscape of cybersecurity in the context of Generative Artificial Intelligence (AI), highlighting the dual-edged nature of technological advancements that offer significant potential for innovation while posing new threats to user security and privacy. We critically analyze the mechanisms through which Generative AI facilitates sophisticated impersonation attacks and privacy breaches, underpinned by a comprehensive review of current and emerging threats. By synthesizing recent research, we identify gaps in traditional cybersecurity approaches and underscore the necessity for novel solutions that are adaptive to the complexities introduced by AI technologies. This paper proposes a multidisciplinary framework that integrates technical, legal, and ethical considerations, aiming to fortify digital ecosystems against AI-driven vulnerabilities. Through methodological rigor, we offer insights into authentication and verification mechanisms that promise to enhance user security without compromising privacy. Our contributions extend beyond theoretical analysis, proposing actionable strategies for stakeholders to implement robust defenses against the misuse of AI. By anticipating future developments in AI technology, this study sets the groundwork for ongoing innovation in cybersecurity practices, ensuring they remain effective in the face of rapidly advancing digital threats.

Keywords: Generative AI, cybersecurity, Privacy breaches, Impersonation attacks, Authentication, Verification mechanisms, digital ecosystems, Legal and ethical considerations, AI-driven vulnerabilities, Multidisciplinary framework.