Secure Cloud Native DevOps and AI for SAP Digital Banking Mobile Healthcare and Cyber Defense

Main Article Content

Gustavo Alonso

Abstract

The rapid evolution of digital banking, open banking ecosystems, and mobile healthcare platforms requires secure, scalable, and intelligent cloud-native infrastructures. This paper presents a secure cloud-native DevOps and AI platform architecture designed to support SAP digital banking environments, interoperable open banking frameworks, and mobile healthcare systems with integrated real-time analytics and advanced cyber defense mechanisms. 


The proposed framework leverages microservices-based architecture, container orchestration, Infrastructure as Code (IaC), and automated CI/CD pipelines to enable continuous innovation while maintaining regulatory compliance and operational resilience. Artificial intelligence and machine learning models are embedded across the DevSecOps lifecycle to support intelligent test automation, deployment risk assessment, anomaly detection, fraud prevention, and predictive performance monitoring. 


Real-time data streaming and event-driven architectures enable low-latency analytics for financial transactions, healthcare monitoring, and API-based open banking services. The platform incorporates zero-trust security principles, AI-driven threat intelligence, automated vulnerability management, and continuous compliance enforcement to mitigate cyber risks across hybrid and multi-cloud ecosystems.


By unifying secure DevOps practices, AI-powered analytics, and cyber defense strategies, the architecture enhances scalability, reliability, and data protection for mission-critical SAP digital banking and mobile healthcare applications, providing a resilient foundation for next-generation financial and healthcare ecosystems

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Articles

How to Cite

Secure Cloud Native DevOps and AI for SAP Digital Banking Mobile Healthcare and Cyber Defense. (2025). International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 8(Special Issue 1), 55-65. https://doi.org/10.15662/IJRPETM.2025.0801810

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