Journal of AI, Data Science and Cyber Systems

Explainable AI (XAI) for Cyber and Data-Critical Decisions describes designing AI systems where human operators can comprehend, trust, and validate the systems and the outputs, particularly in the high-stakes arena of cybersecurity and data-driven systems. Cyber defense and critical data use cases, AI models build assisted decisions for processes such as threat prioritization, access rights determination, fraud detection, or reactive automation, and the lack of transparency of “black box” models can lead to trust erosion and mistakes, and even worse, adverse impacts. The goal of XAI systems is to describe the reasoning for a particular decision, the contributory data points and their degree of influence, and the confidence level regarding the accuracy of the particular decision. Explainable AI (XAI) encompasses a variety of strategies, including rule extraction, model simplification, visual analytics, feature attribution, and causal reasoning. These strategies allow analysts and decision-makers to track the decisions of the AI back to the evidence. In the field of cybersecurity, explainable AI assists in confirming alerts, decreases the number of false positives, aids in the analysis of incidents, and helps to maintain accountability in audits and compliance reviews. In data-critical systems, explainable AI supports the ethical use of systems, the identification of bias, and compliance with regulations. Explainable AI improves trust, transparency, and operational effectiveness in fast and defensible decision-making environments by closing the gap between automated systems and human reasoning.