Holomorphic Data Protection in Federated IT Ecosystems: Synergizing Confidential Computing with AI-Driven Anomaly Detection for Cross-Border Cloud Migrations
International Research Journal of Modernization in Engineering Technology and Science · 2025
In today's interconnected global IT, the protection of data across federated ecosystems is more relevant than ever.This work investigates a holomorphic approach to data protection: seamlessly integrating confidential computing and AI-driven anomaly detection to better secure cross-border cloud migrations.Our work introduces a synergistic approach, first hardening the integrity of the data during the migration process, proactive detection of subtle and potentially malicious anomalies using a publicly available real-world dataset.We propose the use of a continuous and adaptive security model to perform the solution challenges faced due to diverse regulatory frameworks and operational demands in the distributed cloud environment.Ultimately, our findings indicate that holomorphic data protection can change how organizations protect their digital assets and make strong security scalable and people-friendly.