Explainable AI in Cyber Security: Enhancing Model Transparency

Abstract

In today’s rapidly evolving digital landscape, cybersecurity is paramount as organizations face increasingly sophisticated attacks. Artificial intelligence (AI) has become a key tool in detecting and mitigating these threats; however, conventional AI models often operate as “black boxes,” leaving decision processes obscure. Explainable AI (XAI) emerges as a promising solution by illuminating the internal mechanisms of these models, thereby enhancing transparency and trust. This paper explores the integration of XAI into cybersecurity frameworks to improve model transparency and accountability. We examine techniques such as feature importance analysis, surrogate modeling, and visualization methods that reveal how AI systems identify anomalies and flag potential threats. Our analysis demonstrates that making AI decisions interpretable not only supports security experts in understanding and validating automated responses but also aids in regulatory compliance and ethical oversight. Furthermore, enhanced transparency helps in diagnosing biases and vulnerabilities that could be exploited by adversaries, ultimately strengthening the resilience of cybersecurity systems.