Authors: Tenzin Dorji
Abstract: The rapid integration of Artificial Intelligence (AI) into cybersecurity has significantly enhanced threat detection, intrusion prevention, and decision-making capabilities. However, as AI models become increasingly complex, their decision processes often operate as “black boxes,” making it difficult for human analysts to understand, verify, or trust their outcomes. This lack of interpretability poses critical challenges to transparency, accountability, and ethical governance in security decision systems. In recent years, Explainable Artificial Intelligence (XAI) has emerged as a transformative approach to bridge this gap by making AI systems more interpretable and transparent without substantially compromising performance. XAI seeks to ensure that every automated security decision whether related to intrusion detection, access control, or malware classification is supported by understandable and justifiable reasoning. The concept of explainability in AI-based security systems is grounded in the need for trustworthy AI, where users, auditors, and stakeholders can comprehend how and why a system made a particular decision. This is particularly crucial in security domains where decisions have direct implications for privacy, compliance, and risk mitigation. For instance, when an intrusion detection system flags anomalous network behavior, it is not sufficient to merely report the event; analysts must also understand which features or patterns triggered the alert. XAI methods such as Local Interpretable Model-agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP), and attention-based visualization frameworks provide the interpretive mechanisms required for this understanding. These tools offer insights into the model’s internal logic, allowing for greater collaboration between AI systems and human security experts. This review paper explores the theoretical foundations, technical methodologies, and practical implications of XAI in enhancing transparency across diverse security decision systems.
DOI: http://doi.org/10.5281/zenodo.17839883
