Nipuna Thalpage
Cite: Thalpage, N. Explainable AI for Cybersecurity Applications: A Review Article on Techniques, Deployments, and Usability Challenges. JDS, 7(1), 3-10, (2025). https://doi.org/10.33847/2686-8296.7.1_1
Abstract.The growing reliance on Artificial Intelligence (AI) in cybersecurity has elevated concerns about the interpretability and transparency of automated decision-making systems. In environments where trust, accountability, and real-time responsiveness are critical, the “black box” nature of many AI models poses significant barriers to their adoption and operational effectiveness. This systematic literature review examines recent developments in Explainable Artificial Intelligence (XAI) within the cybersecurity domain, focusing on its role in enhancing transparency, trust, and human-AI collaboration. A structured search was conducted across six major academic databases and preprint repositories, yielding nine peer-reviewed studies that met rigorous inclusion criteria. These studies were analyzed across five quality dimensions: relevance, clarity of XAI methods, empirical grounding, human factors consideration, and deployment realism. Findings reveal that while technical innovations—such as SHAP, LIME, Grad-CAM, and lightweight edge-based models—offer substantial gains in model transparency, these advances often fail to translate into actionable insights for end-users due to limitations in cognitive usability and system integration. The review identifies a recurring gap between the theoretical promise of XAI and its practical implementation in real-world security infrastructures. Studies highlight issues such as user disengagement, underutilization of explanation tools, and inadequate alignment with operational workflows. Emerging directions emphasize the need for user-centered design, co-explainability frameworks, and interdisciplinary approaches that incorporate cognitive science and human-computer interaction. In conclusion, the future of XAI in cybersecurity hinges on its ability to go beyond algorithmic transparency and embed interpretability within the social, cognitive, and organizational contexts in which security professionals operate. Bridging these gaps will be essential for realizing the full potential of explainable AI systems as trustworthy and effective tools in modern cybersecurity operations.
Keywords: Explainable AI, Cybersecurity, Artificial Intelligence.
References
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Published online 25.06.2025