Securing Artificial Intelligence Models: A Comprehensive Cybersecurity Approach

Abstract

As artificial intelligence (AI) becomes integral to diverse applications, the imperative to secure AI models against evolving threats has gained paramount importance. This paper presents a novel cybersecurity framework tailored explicitly for AI models, synthesizing insights from a comprehensive literature review, real-world case studies, and practical implementation strategies. Drawing from seminal works on adversarial attacks, data privacy, and secure deployment practices, the framework addresses vulnerabilities throughout the AI development lifecycle. Preliminary results indicate a significant enhancement in the resilience of AI models, demonstrating reduced success rates of adversarial attacks, effective data encryption, and robust secure deployment practices. The framework's adaptability across diverse use cases underscores its practicality. These findings mark a crucial step toward establishing comprehensive and practical cybersecurity measures, contributing to the ongoing discourse on securing the expanding field of artificial intelligence. Ongoing efforts involve further validation, optimization, and exploration of additional security measures to fortify AI models in an ever-changing threat landscape.