An AI-Enabled Cybersecurity Architecture for Digital Banking with Real-Time Analytics and SAP Integration using Cloud-Native Machine Learning

Abstract

The rapid digital transformation of banking services has elevated both operational efficiency and cyber‑risk exposure. Digital banking platforms must defend against increasingly sophisticated threats—including fraud, insider threats, malware, and advanced persistent attacks—while also providing real‑time analytics that support decision making and regulatory compliance. Integrating Artificial Intelligence (AI) for cybersecurity improves detection, prediction, and mitigation of threats beyond traditional rule‑based systems.

 This research proposes a comprehensive AI‑enabled cybersecurity architecture tailored for digital banking systems that integrates real‑time analytics with SAP enterprise financial systems. The architecture leverages machine learning and deep learning models for anomaly detection, behavioral threat profiling, and predictive risk scoring, fused with SAP analytics components to ensure secure access to core financial operational data and business logic. Real‑time data pipelines and event streams feed both security models and business analytics, enabling proactive defense and automated response strategies.

The design also incorporates layered security controls such as zero‑trust access, encryption, and continuous monitoring to protect data at rest and in motion. Simulation results demonstrate enhanced threat detection accuracy, reduced response latency, and improved integration with financial decision systems powered by SAP analytics. Recommendations for deployment, governance, and future enhancements are discussed, illustrating a scalable, secure, and data‑driven cybersecurity framework for modern digital banking.