Mitigating the Misuse of Generative AI: Navigating the Emerging Threat Landscape and Modern Security Paradigms
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© 2024 by IJCTT Journal
Volume-72 Issue-9
Year of Publication : 2024
Authors : Sharat Ganesh
DOI :  10.14445/22312803/IJCTT-V72I9P103

How to Cite?

Sharat Ganesh, "Mitigating the Misuse of Generative AI: Navigating the Emerging Threat Landscape and Modern Security Paradigms," International Journal of Computer Trends and Technology, vol. 72, no. 9, pp. 14-17, 2024. Crossref, https://doi.org/10.14445/22312803/IJCTT-V72I9P103

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.

Keywords
Data Poisoning, Generative AI, Model Protection, Cybersecurity, Security Risks.

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