Graph Neural Networks for Intelligent Enterprise Integration and Adaptive Governance in Federated Cloud Environments

Abstract

Federated cloud environments enable enterprises to combine resources, applications, data, and services distributed across multiple cloud providers, private data centers, and organizational domains. However, the heterogeneous and decentralized nature of federated clouds introduces significant challenges in enterprise integration, policy coordination, security management, resource optimization, and governance. Traditional governance mechanisms generally depend on static policies and centralized monitoring, which may be inadequate for dynamic cloud ecosystems. Graph Neural Networks (GNNs) provide a promising artificial intelligence approach because they can model complex relationships among applications, services, users, resources, policies, and communication channels as interconnected graphs. This paper investigates the application of GNNs for intelligent enterprise integration and adaptive governance in federated cloud environments. The proposed approach represents cloud resources and organizational entities as graph nodes and their dependencies, interactions, trust relationships, and data flows as graph edges. GNN-based models can then analyze changing relationships, identify anomalous behavior, predict resource requirements, support policy decisions, and recommend adaptive governance actions. The research methodology combines graph construction, feature extraction, GNN model development, simulation, experimental evaluation, and comparative analysis against conventional governance techniques. The study considers integration efficiency, anomaly detection, policy compliance, resource utilization, scalability, and decision accuracy as major evaluation criteria. The proposed framework demonstrates how graph-based intelligence can strengthen decentralized enterprise governance while maintaining flexibility and interoperability across federated cloud infrastructures.