AI-Powered Middleware Mesh for Real-Time Multi-Cloud Integration Governance: A Graph Neural Network Approach

Distributed service-based architectures present governance, compliance, and observability issues for enterprise multi-cloud deployments. This project presented AI-Powered Middleware Mesh (AIMM), which includes a smart governance layer, leveraging dynamic dependency modeling via Graph Neural Networks and proactive AI policy engines to support compliance validation. AIMM plugs into existing service mesh infrastructure to enable real-time policy enforcement, proactive identification of future security violations, and root-cause analysis automation of integration failures while processing voice, data, and video. The architecture relies on explainable AI modules to provide transparency around its middleware decisions while maintaining enterprise-grade performance indicators. The conducted comprehensive evaluations with AIMM across healthcare, financial services, manufacturing, and government settings that demonstrate AIMM reduces security violations and improves failure detection accuracy while preserving low-latency communication paths across multiple cloud services. AIMM creates a solution to assess the behavioural tendencies of security violations, proactively reduces failures, and fosters real-time observability of multi-cloud services shared by many enterprise services while performing at the enterprise level in security and compliance across regulatory environments. AIMM fills vital gaps in middleware solutions today for autonomous governance that scale with the complexity of the actual enterprise and the regulatory controls enforced across a spectrum of environmental and security compliance frameworks.