Volume 15 , Issue 1 , PP: 288-297, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
M. N. V Kiranbabu 1 * , A. Jeraldine Viji 2 , Amit Kumar Chandanan 3 , Vijay Birchha 4 , Tushar Kumar Pandey 5 , Sumit Kumar Sar 6
Doi: https://doi.org/10.54216/JCIM.150123
As AI is deployed increasingly in defensive systems, hostile assaults have increased. AI-driven defensive systems are vulnerable to attacks that exploit flaws. This article examines the approaches used to resist AI-based cybersecurity systems and their effects on security. This paper examines existing literature and case studies to demonstrate how attackers modify AI models. These include avoidance, poisoning, and data-driven assaults. It also considers data breaches, system failures, and unauthorized access if a hostile effort succeeds. The report recommends adversarial training, model testing, and input sanitization to address these issues. It also stresses the need for monitoring and updating AI algorithms to adapt to changing opponent tactics. This paper emphasizes the need to limit hostile strike threats using real-life examples and statistics. To defend AI-driven cybersecurity systems from complex threats, cybersecurity specialists, AI researchers, and policymakers must collaborate across domains. This article provides full guidance for cybersecurity and AI professionals. It describes the complex issues adversarial assaults create and proposes a flexible and robust architecture to safeguard AI-driven cybersecurity systems from emerging threats.
Adversarial attacks , AI-driven , cybersecurity systems , challenges , threats , vulnerabilities , defense mechanisms , data confidentiality , interdisciplinary collaboration , resilient framework
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