Enhanced Data Leakage Detection in Cloud and Enterprise Environments through NLP-Driven Classification, Metadata Tagging, and Anomaly-Based Learning

Data leakage occurs when information from outside a training dataset inadvertently influences a machine learning model, leading to overly optimistic performance estimates and reduced generalizability. Detecting data leakage is crucial to maintain model integrity, prevent overfitting, and ensure accurate deployment results in real-world applications. Traditional methods for leakage detection are limited by their inability to capture subtle, complex forms of leakage that arise in highdimensional data or intricate workflows. This study proposes an improved data leakage detection framework that leverages a combination of statistical testing, cross-validation anomaly checks, and interpretability techniques. Our approach systematically identifies suspicious patterns, assesses feature-target relationships across training and test sets, and flags inconsistent data flows that may signal leakage. By implementing these methods, we demonstrate enhanced sensitivity to various leakage types, including label, feature, and temporal leakage, across several case studies in healthcare, finance, and image processing. Our findings highlight the importance of robust leakage detection techniques in developing reliable machine learning models and suggest practical guidelines for integrating these methods into machine learning pipelines. This approach ultimately promotes the development of models with better generalizability, fairness, and trustworthiness.