ADAPTIVE AND AUTOMATED DATA ANONYMIZATION FRAMEWORKS FOR MULTI-CLOUD ENVIRONMENTS
Krupal Gangapatnam · INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND INFORMATION TECHNOLOGY · 2025
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 immediate improvements and future developments.The findings demonstrate that robust anonymization frameworks significantly enhance data protection, operational efficiency, and compliance management across multi-cloud environments.