A Secure AI-Driven Cloud Architecture for Modern Digital Infrastructure Integrating GitOps-Governed Data Platforms and Financial Systems

Abstract

In the era of accelerating digital transformation, secure and resilient cloud architecture has become essential for modern enterprises, particularly those integrating complex data platforms and mission-critical financial systems. This paper proposes a comprehensive secure AI-driven cloud architecture that leverages GitOps governance to ensure consistent deployments, automated compliance, and adaptive security controls. The architecture synergizes multiple cutting-edge technologies: containerized microservices, machine learning-based anomaly detection, policy-as-code governance, and continuous delivery pipelines to support scalable data ecosystems and resilient financial workflows. Through a hybrid cloud model complemented by policy enforcement and real-time threat detection, the architecture enhances confidentiality, integrity, and availability while reducing operational overhead.
This research conducts a systematic literature review, designs a methodology for implementation and evaluation, and demonstrates benefits and limitations. Results highlight significant improvements in deployment consistency, security posture, and system resilience. The study concludes with recommendations for future research on federated learning integration and cross-domain compliance automation. Findings are relevant to architects, engineers, and operational leaders seeking robust, governed, and AI-enhanced cloud infrastructures in highly regulated domains.