The Role of Explainability in Detecting Adversarial Attacks on AI-Powered Cybersecurity Systems

Abstract

Artificial Intelligence (AI) has become increasingly central to modern cybersecurity systems, enabling adaptive threat detection, anomaly recognition, and predictive defense mechanisms. However, the widespread deployment of AI introduces new vulnerabilities, particularly through adversarial attacks that exploit model weaknesses to bypass detection. Explainable AI (XAI) methods have emerged as critical tools for understanding, validating, and fortifying AI models against such attacks. This paper examines the role of explainability in detecting adversarial attacks on AI-powered cybersecurity systems. We explore the theoretical foundations of XAI, its application in intrusion detection systems, anomaly detection, and threat intelligence, and analyze current methodologies for adversarial detection. The integration of XAI with cybersecurity enables enhanced transparency, accountability, and robustness, ensuring that AI models not only detect malicious activity but also provide interpretable insights for human operators. Challenges, limitations, and future research directions are discussed, highlighting the potential of XAI as a cornerstone for resilient and trustworthy cybersecurity.