Developing Sustainable Data Retention Policies: A Machine Learning Approach to Intelligent Data Lifecycle Management

Abstract

In the era of rapidly growing data, organizations face increasing challenges in managing vast volumes of information while adhering to regulatory requirements and optimizing storage costs. Developing sustainable data retention policies is crucial for efficient data management and ensuring compliance with privacy laws and industry regulations. This paper explores a machine learning (ML)-driven approach to intelligent data lifecycle management, offering a framework for creating sustainable data retention policies. By leveraging ML algorithms, the proposed system automates the classification, retention, and deletion of data based on predefined business rules, compliance standards, and usage patterns. This approach enhances data governance, reduces operational costs, and improves the efficiency of data management practices.