Enhancing Federated Cloud with Explainable Generative Artificial Intelligence for Enterprise Data Modernization and Governance

Abstract

Modern enterprises are increasingly adopting federated multi-cloud environments to prevent vendor lock-in, maintain localized data sovereignty, and leverage specialized infrastructure across geographic regions. However, managing distributed data estates across heterogeneous cloud architectures introduces severe operational bottlenecks in data discovery, schema standardization, compliance enforcement, and cross-silo lineage tracking. Traditional centralized data governance models introduce performance friction and struggle to scale alongside high-velocity, unstructured data assets. This paper presents a novel framework integrating Explainable Generative Artificial Intelligence (xGenAI) into federated cloud architectures to automate enterprise data modernization and continuous governance. By embedding localized retrieval-augmented generative models and automated ontology mappers at distributed cloud nodes, the framework dynamically generates unified semantic metadata, maps cross-cloud lineage, and enforces fine-grained access policies. To ensure transparency and regulatory compliance, the system incorporates Shapley Additive exPlanations (SHAP) and attention-map attribution mechanisms, offering human-interpretable reasoning for automated classification, policy decisions, and synthetic data generation. Empirical evaluation across a simulated multi-cloud ecosystem demonstrates significant improvements in metadata auto-cataloging throughput, drastic reductions in policy audit latency, and enhanced explainability metrics over conventional governance toolchains.