Engineering Secure, Insight-Driven Analytics for Multi-Cloud Governance: A Strategic Framework for Consumer-Centric Intelligence in Distributed Systems

Abstract

In the era of digital transformation, managing secure and intelligent analytics across multi-cloud environments presents both a technical and strategic challenge. This study proposes an integrated framework for engineering secure, insight-driven analytics aimed at enhancing multi-cloud governance and enabling consumer-centric intelligence within distributed systems. The framework combines security-by-design architecture, federated identity management, and AI-powered analytics to address core issues of data privacy, policy compliance, and user engagement. Using a mixed-methods approach, the framework was developed and validated through empirical testing across three case studies in finance, healthcare, and e-commerce. Key findings demonstrate a substantial reduction in policy violations (over 78%), significant improvements in uptime and access latency, and enhanced consumer perception in transparency, trust, and personalization. Machine learning models such as Random Forest and SVM yielded high predictive accuracy, supporting real-time behavioral analytics. The research confirms that aligning secure governance with intelligent analytics and user expectations can drive operational efficiency and foster trust in complex, distributed ecosystems. The results offer valuable insights for organizations seeking to balance scalability, compliance, and personalization in multi-cloud infrastructures.