Automated data anonymization tools to comply with GDPR regulations, processing billions of data points stored across multiple cloud environments

Gangapatnam, Krupal (2025) Automated data anonymization tools to comply with GDPR regulations, processing billions of data points stored across multiple cloud environments. International Journal of Science and Research Archive, 14 (1). pp. 787-796. ISSN 2582-8185

The rapid evolution of data processing demands has led to innovative approaches in enterprise-scale data anonymization and protection. This comprehensive examination explores the implementation of Delphix across diverse cloud environments, focusing on its technical architecture, performance metrics, and compliance features. The platform demonstrates exceptional capabilities in handling sensitive data through advanced machine learning algorithms and sophisticated processing pipelines. The architecture incorporates robust security mechanisms, parallel processing capabilities, and intelligent resource optimization across multiple geographical regions. Integration with major cloud providers enables seamless scalability while maintaining strict data protection standards. The implementation showcases significant improvements in processing efficiency, reduced data breach risks, and enhanced compliance adherence through automated controls. Best practices and deployment guidelines ensure optimal performance through carefully calibrated infrastructure requirements and monitoring systems. The solution addresses the critical challenges of data privacy and security while maintaining high throughput rates and system availability across distributed environments.