TRANSFORMING CYBER DEFENSE THROUGH EXPLAINABLE AI: INTERPRETABILITY IN SECURITY CONTEXTS

Abstract

Artificial Intelligence (AI) plays an increasingly vital role in modern cybersecurity, enabling faster detection of threats, automated responses, and adaptive defense mechanisms. Many AI models function as black boxes, lacking transparency and interpretability an issue that significantly limits their adoption in critical security contexts where accountability, trust, and human decision-making are essential. This paper investigates the transformative impact of Explainable AI (XAI) in cyber defense, focusing on how interpretability can enhance threat detection, support compliance, and empower analysts to make informed decisions. I provide a comprehensive overview of XAI techniques, including SHAP, LIME, counterfactual explanations, and saliency maps, and evaluate their effectiveness in applications such as intrusion detection, malware classification, and phishing detection. A novel framework is proposed for integrating XAI into existing security architectures, emphasizing user-centric explanations and real-time decision support. I demonstrate that incorporating XAI not only improves model transparency but also strengthens operational effectiveness. The paper concludes with a discussion on current challenges, such as adversarial risks and cognitive burden, and outlines future directions for research, policy, and governance. My findings suggest that explainability is not just an enhancement, but a fundamental requirement for trustworthy and resilient cyber defense systems.