Authors: Moaiz Kazi, Sufiyan Ansari, Arhaan Shaikh, Usaid Khairdi
Abstract: The modern digital world is generating unprece-dented amounts of unstructured data, which has given rise to “digital hoarding.” Cloud users often feel overwhelmed by massive file repositories, experience slow searches, and see their organized systems break down. To tackle this, we introduce XAI-CloudAssist, a robust, cloud-native assistant designed to automatically classify files using an ensemble of Random Forest models. Unlike traditional automated systems that operate as black boxes, our approach puts transparency first by integrating Explainable AI. We use SHAP values to reveal the reasoning behind each classification, assigning quantitative importance scores to metadata features. In experiments, the system achieved 98 Percentage classification accuracy, showing that metadata-driven cues like storage footprint and access frequency are highly predictive. Moreover, XAI-CloudAssist provides localized expla-nations for every categorization, helping users understand the de-cisions and fostering trust in cloud automation. Keywords: Cloud Computing, File Organization, Machine Learning, Explainable AI, SHAP, Random Forest, Automation, Data Governance.
