Risk Assessment Framework for Data Leakage Prevention Using Machine Learning Techniques

Abstract

Data leakage remains a critical concern for organizations handling sensitive information, requiring effective risk assessment methods to identify potential vulnerabilities. This paper proposes a machine learning-based framework for assessing data leakage risks in corporate environments. Our approach focuses on analyzing user access patterns and data flow characteristics to identify anomalous behaviors that may indicate potential leakage risks. We employ anomaly detection algorithms, particularly Isolation Forest and Local Outlier Factor, to detect unusual data access activities. The framework includes a risk scoring mechanism that evaluates access requests based on user roles, data sensitivity levels, and contextual factors. Additionally, we explore the use of natural language processing for identifying sensitive content in unstructured documents, enabling more comprehensive risk assessment. The proposed method provides organizations with a practical tool for prioritizing security efforts and allocating resources to high-risk areas. This work supports data protection compliance requirements and helps organizations strengthen their data governance practices.