International Research Journal of Education and Technology

The extensive deployment of artificial intelligence throughout cybersecurity created innovative protection systems that promptly identify attacks from large data sets. Yet, this AI-intensive approach has generated new threats for attackers to abuse sophisticated techniques for security breaches and data protection breaches. This study examines how adversarial AI attacks aim at cybersecurity systems through their operational methods, which results in adverse effects on organizational operations and individual users. The research includes a study of multiple defensive methods together with mitigation approaches that aim to protect adversarial AI systems through improved AI security system durability. Thus, this paper unites existing research with upcoming trends so that it can deliver a full comprehension of adversarial AI's cybersecurity challenges while directing improved security solution development. As organizations expand their adoption of AI, their vulnerable points become exposed to cyber threats. AI attacks based on adversarial tactics use specially made inputs that make AI systems misinterpret information, thus producing incorrect outputs or degrading their functionality to the point of failure. Different adversarial attacks occur within the spectrum of manipulations that include medical image counterfeiting for cancer diagnosis, along with misbehaving traffic signals that endanger autonomous vehicle operation.  These vulnerabilities represent major threats to cybersecurity systems because they compromise the protective functions that were established for sensitive information and critical infrastructure. 

Keywords: Adversarial AI, Cybersecurity, Data Protection, Machine Learning, Threat Detection, Attack Mitigation