Explainable AI (XAI) for Security Decisions to Mitigate Cybersecurity Attacks

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.