Federated AI Governance Mesh for Multi-Cloud Platforms: Enabling Policy-Driven Compliance and Real-Time Data Trust Across Autonomous Systems

The matter of disjointed governance strategies is tackled herein, specifically how these strategies hinder consistent compliance and dependable trust across self-governing systems operating within multi-cloud setups. A potential solution is introduced: a federated AI governance structure [citeX] designed to streamline policy-based adherence and guarantee current data trust, facilitating frictionless functionality spanning different cloud infrastructures. Employing a mixed-methods strategy, this research compiles and examines both qualitative and quantitative information [extractedKnowledgeX] concerning present governance procedures, compliance benchmarks, and trust conventions. The inquiry brings to light notable discrepancies in the execution of existing governance models, underscoring the pressing demand for a unified approach. The core discoveries suggest that a centralized governance framework can alleviate ambiguity in compliance and fortify data correctness, consequently cultivating amplified stakeholder assurance, notably within the healthcare sphere where data precision and confidentiality stand as utmost [citeX]. Furthermore, it is indicated that the proposed framework not only rectifies current shortcomings in governance but also establishes a fundamental blueprint for subsequent uses across varied sectors dependent upon cloud technologies. The repercussions of this investigation are substantial, presenting a route to reinforce adherence to policy and cultivate trust across autonomous systems, ultimately endorsing enriched data use in healthcare and beyond, improving patient results, and assuring observance to regulations in an ever-more digital and interconnected global landscape. II. Keywords Federated AI Governance, Multi-Cloud Platforms, Policy-Driven Compliance, Real-Time Data Trust, Autonomous Systems, AI Ethics and Accountability, Decentralized Governance, Machine Learning for Compliance, Cloud Data Integrity, Regulatory Frameworks