End-to-End Governance Strategies for Secure Multi-Domain Cloud Analytics

Pramod Raja Konda

Abstract


The rapid expansion of cloud-based analytical ecosystems has intensified the need for robust, end-to-end governance strategies capable of securing data across multiple domains, organizations, and jurisdictions. As cloud analytics increasingly integrates diverse data sources—from enterprise systems and IoT devices to public datasets and cross-organizational platforms—governance frameworks must ensure confidentiality, compliance, integrity, and interoperability without hindering analytical performance. This research investigates governance mechanisms spanning identity management, data classification, policy enforcement, lineage tracking, and multi-domain access control. Through architectural modeling and a synthetic multi-domain case study, the study demonstrates that distributed policy orchestration, zero-trust access layers, and automated compliance monitoring significantly reduce governance risk while enabling efficient, scalable analytics. The results highlight the importance of unified metadata standards, cross-domain trust anchors, and AI-assisted governance automation in building secure, compliant, and resilient multi-domain cloud analytics ecosystems.

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References


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