Data-Driven Explainable AI In Cyber Security Awareness In Nepal

Abstract

Cybersecurity expertise is essential in ensuring digital safety, especially in developing economies like Nepal, where the rapid uptake of technology increases vulnerability to cyberattacks. Legacy methodologies such as rule-based systems and human-resource-intensive threat analysis are defeated by issues like scalability issues, lagging response, and poor interpretable insight, thereby being less effective in mitigating dynamically evolving threats. In order to counter such limitations, the current study proposes a Data-Driven Explainable AI (XAI) system to promote cybersecurity awareness in Nepal. The principal aim is to create an explainable AI-based system that guides users, policymakers, and cybersecurity professionals in cyber threat understanding and prevention more efficiently. Unlike black-box models whose internal mechanisms are unknown, the proposed framework utilizes SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-agnostic Explanations) to render explainable, actionable knowledge about cyber risks. The architecture includes data preprocessing, model training, integration of explainability layer, and real-time cyber risk assessment, facilitating continuous learning and adaptive threat intelligence. Early results show that XAI approaches improve model interpretability, build user trust, and enhance cybersecurity consciousness through open threat analysis. The study establishes the revolutionary promise of explainable, data-driven AI solutions to revolutionize Nepal's cybersecurity and prepare users for future digital threats.