ADAPTIVE AND AUTOMATED DATA ANONYMIZATION FRAMEWORKS FOR MULTI-CLOUD ENVIRONMENTS

This technical article explores the implementation of adaptive and automated data anonymization frameworks across diverse computing environments, focusing on mainframe, SAP ERP, and cloud platforms. The article examines current data security challenges, core anonymization techniques, including K-anonymity, L-diversity, and Tcloseness, and the Delphix implementation framework for automated data masking. The article investigates platform-specific considerations, benefits of automation, and comprehensive monitoring systems while providing insights into immediateimprovements and future developments.The findings demonstrate that robust anonymization frameworks significantly enhance data protection, operational efficiency, and compliance management across multi-cloud environments.