In the evolving landscape of cybersecurity, the integration of Artificial Intelligence (AI) for threat detection and response has become increasingly prevalent. However, the opaque nature of many AI models poses a significant challenge in high-stakes environments where transparency, trust, and accountability are critical. This study explores the application of Explainable Artificial Intelligence (XAI) to enhance security decision-making and mitigate cybersecurity attacks. By providing human-understandable insights into model outputs, XAI can bridge the gap between automated systems and cybersecurity analysts, fostering greater trust and operational efficiency. The paper reviews current AI-driven cybersecurity solutions, identifies the limitations in interpretability, and demonstrates how XAI frameworks such as LIME, SHAP, and counterfactual explanations can support threat identification, anomaly detection, and incident response. A case study using a real-world intrusion detection dataset is presented, showcasing how XAI improves detection transparency without sacrificing accuracy. The findings highlight the dual benefit of XAI: maintaining strong security performance while providing interpretability that supports compliance, auditability, and human oversight. This work underscores the importance of explainability as a foundational requirement for AI-based cybersecurity systems, particularly in environments demanding robust, real-time, and accountable threat mitigation.
Sahito et al. (Wed,) studied this question.