AI and Machine Learning Driven Multi-Cloud Enterprise Platforms for Digital Banking Renewable Energy and Secure Mobile Systems
DOI:
https://doi.org/10.15662/IJEETR.2025.0705011Keywords:
Artificial Intelligence, Machine Learning, Multi-Cloud Architecture, Enterprise Platforms, Digital Banking, Renewable Energy Integration, Secure Mobile Systems, Cloud Security, DevSecOps, API Governance, Financial Risk Analytics, Edge Computing, Zero Trust Architecture, Blockchain Integration, Intelligent AutomationAbstract
The convergence of artificial intelligence (AI), machine learning (ML), and multi-cloud computing is transforming enterprise platforms across digital banking, renewable energy systems, and secure mobile ecosystems. Modern enterprises operate in highly dynamic environments characterized by regulatory pressures, cybersecurity threats, fluctuating energy demands, and evolving customer expectations. Multi-cloud architectures—leveraging services across public, private, and hybrid cloud infrastructures—enable resilience, scalability, and vendor flexibility. When combined with AI-driven analytics and machine learning automation, these platforms support predictive decision-making, fraud detection, intelligent energy management, and secure mobile service delivery.
In digital banking, AI enhances fraud detection, credit scoring, customer personalization, and real-time transaction monitoring. Renewable energy platforms use ML for load forecasting, grid optimization, predictive maintenance of solar and wind assets, and carbon footprint analytics. Secure mobile systems integrate AI-driven behavioral authentication, anomaly detection, and zero-trust security enforcement to protect distributed endpoints. However, integrating these capabilities across multi-cloud ecosystems introduces challenges including data governance, interoperability, latency management, model lifecycle control, compliance alignment, and cross-cloud security orchestration.
This paper proposes a unified AI and ML-driven multi-cloud enterprise architecture that integrates container orchestration, data lakes, federated identity management, automated DevSecOps pipelines, and secure API governance. The methodology includes architectural modeling, threat analysis, performance simulation, and compliance mapping across financial, energy, and mobile security domains. The proposed framework emphasizes zero-trust networking, distributed ML pipelines, workload portability, and policy-driven governance across cloud providers.
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