AI Driven Multi-Cloud Architecture for Secure Enterprise Data and Autonomous Predictive Analytics
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Abstract
The rapid digital transformation of enterprises has resulted in massive data generation across distributed systems, cloud services, and connected devices. Traditional data platforms often struggle to ensure security, scalability, interoperability, and real-time intelligence. This study proposes an Intelligent Artificial Intelligence-driven Multi-Cloud Architecture designed to support secure enterprise data platforms, predictive risk analytics, and autonomous digital ecosystems. The proposed architecture integrates artificial intelligence, multi-cloud computing, secure data governance, and automated orchestration mechanisms to create a resilient digital infrastructure capable of handling complex enterprise workloads.
The framework leverages machine learning models for predictive analytics, enabling organizations to detect risks, anomalies, and cyber threats before they impact operational processes. Additionally, the architecture supports autonomous digital ecosystems by enabling intelligent data pipelines, adaptive security mechanisms, and cross-cloud resource optimization. By combining distributed data platforms with AI-enabled decision intelligence, enterprises can achieve higher levels of efficiency, scalability, and operational resilience.
The research explores architectural components, risk prediction models, and governance mechanisms within multi-cloud environments. It also analyzes the advantages and limitations of AI-driven enterprise cloud platforms. The findings highlight that integrating intelligent analytics with secure multi-cloud infrastructure can significantly enhance enterprise data management, predictive risk mitigation, and digital ecosystem automation, enabling organizations to achieve sustainable digital transformation in highly dynamic technological environments.
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References
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