Data cloud security in the digital age: Leveraging machine learning for breach detection and risk mitigation

Abstract:

The protection of sensitive data stored in cloud environments represents a top priority for organizations. This document proposes a complete predictive framework designed to boost cloud data security through machine learning method implementation. The complete framework contains several essential elements starting with data classification while performing sensitivity mapping then encryption and access control followed by breach detection systems. The models including Random Forest together with Logistic Regression and Isolation Forest assist in conducting sensitivity-based file classification and forecasting future data breach occurrences. This model system obtained evaluation scores of Accuracy = 0.92 while reaching Precision = 0.91 and Recall = 0.89 and F1-Score = 0.90. The model achieved excellent classification performance based on the AUC values obtained from ROC curve analysis which reached 0.94 and from Precision-Recall curve analysis which reached 1.00. The visualization tools incorporate bar charts alongside heatmaps to display data sensitivity spreads as well as identify breach occurrences effectively. This framework provides a flexible method for cloud data protection which both automatically finds security weaknesses and strengthens access protocols while securing important data. Additional machine learning technology integration with real-time threat warning systems should be studied as potential solutions to advance cloud data protection methods.