AI-Driven Cloud-Native Architectures for Secure and Compliant Digital Banking: Integrating Deep Learning and Data Governance

Abstract

Cloud-native architectures are increasingly adopted in digital banking to achieve agility, scalability, and resilience. However, with these benefits come significant security, privacy, and compliance challenges, especially when introducing AI and deep learning components. This paper investigates how cloud-native architectures can be designed and governed so as to support secure, compliant digital banking services that leverage AI and deep learning. We propose a layered architecture that integrates governance policies, embedded security, data protection, observability, and risk management with advanced AI/deep learning models. Through literature survey, case studies, and experimental prototyping, we assess how such architectures manage regulatory requirements (e.g. GDPR, CCPA, PCI‑DSS), ensure model fairness, transparency, and auditability, and support secure deployment pipelines. We also explore privacy‑preserving AI techniques such as federated learning, differential privacy, and secure multi‑party computation. Our findings show that properly governed cloud-native systems can significantly reduce risk of data breaches, improve compliance readiness, and maintain model performance while respecting privacy constraints. We identify trade‑offs involved, including latency, cost overheads, operational complexity, and potential degradation of model accuracy under strong privacy constraints. The paper concludes with best practices, key architectural components, policy recommendations, and directions for future work.