Mitigating the Misuse of Generative AI: Navigating the Emerging Threat Landscape and Modern Security Paradigms
Year: 2024
Type: article
Abstract: Generative AI has emerged as a transformative technology with wide-ranging applications across industries. However, its capabilities also introduce significant security risks that must be carefully managed. This paper examines the key threats facing generative AI systems, including data poisoning, model stealing, and adversarial attacks. It outlines a modern security paradigm to mitigate these risks, encompassing data quality and validation, model protection, adversarial robustness, and continuous monitoring. Through an analysis of recent case studies and emerging research, the paper argues that a comprehensive, multi-layered approach to security is essential for realizing the benefits of generative AI while minimizing potential negative impacts. The consequences of security breaches, including reputational damage, financial losses, and potential national security implications, are discussed. The findings highlight the need for ongoing vigilance and collaboration across the AI community to address the evolving threat landscape. This research contributes to the growing body of knowledge on AI security and provides practical insights for developers, users, and policymakers involved in the deployment of generative AI technologies.
Author: Sharat Ganesh
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4403514497