Secure Enterprise Data Fabric using Federated Learning and Sovereign Cloud Architecture for Privacy Engineering

Abstract

The rapid adoption of cloud computing, artificial intelligence, and distributed data ecosystems has created significant opportunities for enterprises while simultaneously intensifying concerns surrounding data privacy, sovereignty, security, and regulatory compliance. This paper proposes a secure enterprise data fabric that integrates federated learning with sovereign cloud architecture to establish a privacy-oriented framework for distributed enterprise data management and analytics. The proposed approach enables organizations to retain sensitive datasets within jurisdictionally controlled environments while allowing machine-learning models to learn collaboratively without requiring raw data to be centrally transferred. Sovereign cloud principles are incorporated through jurisdiction-aware data residency, controlled infrastructure ownership, identity governance, encryption, auditability, and policy-based access mechanisms. Federated learning complements these controls by decentralizing model training and reducing unnecessary exposure of confidential information. The research methodology adopts a mixed conceptual and experimental approach involving architecture design, threat modeling, privacy assessment, security evaluation, and comparative performance analysis. Key evaluation dimensions include data exposure, privacy preservation, model utility, communication overhead, latency, compliance alignment, and resilience against adversarial attacks. The proposed data fabric is intended to provide enterprises with a scalable foundation for privacy engineering in multi-cloud and cross-organizational environments. The study demonstrates how federated intelligence and sovereign infrastructure can be integrated into a unified security architecture that balances analytical utility with organizational and regulatory requirements.