Engineering Secure, Insight-Driven Analytics for Multi-Cloud Governance: A Strategic Framework for Consumer-Centric Intelligence in Distributed Systems
The rapid expansion of digital ecosystems and the growing reliance on multi-cloud environments by modern enterprises have introduced a complex array of data governance and security challenges. This paper introduces a comprehensive, security-aware analytics framework that leverages Artificial Intelligence (AI) and Machine Learning (ML) to orchestrate intelligent data processing across distributed cloud platforms. The framework is specifically designed to integrate consumer behavior insights derived from survey data and digital touchpoints into an engineering analytics pipeline. This facilitates real-time decision-making, ensures regulatory compliance, and strengthens enterprise resilience against cyber threats. By bridging the gap between data intelligence, cloud infrastructure, and consumer-centric strategies, the proposed model redefines how organizations can govern data across multiple environments while prioritizing security and innovation.
Keywords: Security, AI, Cloud, Engineering Management, AI/ML, Data Engineering, Data Analytics, Consumer Insights, Data Analysis, Surveys
