Secure Cloud Native Architecture for Enterprise Banking and Healthcare Systems with AI Support

Abstract

The increasing demand for digital services in banking and healthcare sectors has accelerated the adoption of cloud-native technologies and artificial intelligence. Organizations are required to manage large volumes of sensitive data while ensuring security, regulatory compliance, scalability, and real-time analytics. Traditional monolithic systems often fail to provide the flexibility and performance needed for modern enterprise environments. This research proposes an AI-enabled secure cloud-native framework designed to support enterprise banking and healthcare systems while enabling intelligent analytics and scalable digital transformation.
The proposed framework integrates artificial intelligence, microservices architecture, containerization, and cloud-native infrastructure to provide a secure and scalable data platform. AI algorithms are employed to analyze enterprise data, detect anomalies, and support predictive decision-making processes. In addition, security mechanisms such as encryption, identity and access management, and automated threat detection are integrated into the architecture to protect sensitive financial and healthcare information.
The study presents the architectural design, implementation strategy, and evaluation of the proposed framework. The research demonstrates how cloud-native technologies combined with AI-driven analytics can enhance operational efficiency, improve data security, and enable intelligent automation in enterprise environments. The results indicate that AI-enabled cloud-native systems can significantly support digital transformation initiatives in banking and healthcare sectors by providing scalable, secure, and intelligent data infrastructures.