Data Modernization in Banking: AI-Driven NFV for Regulatory Compliance and Security

Abstract

: In an era marked by escalating cyber threats and stringent regulatory mandates, banks are increasingly modernizing their data infrastructure. One promising approach involves integrating artificial intelligence (AI) with network function virtualization (NFV) to bolster regulatory compliance and enhance security frameworks. This study explores the convergence of AI-driven NFV technologies in banking, examining their capacity to automate compliance monitoring, enforce dynamic security policies, and streamline data governance. We develop a dual-layer model: an NFV-based network overlay that dynamically deploys virtualized security and compliance functions, and an AI engine that analyzes traffic, detects anomalous patterns, and triggers adaptive NFV policy adjustments. Employing a prototype within a simulated banking network, we assess performance across compliance metrics (e.g., audit traceability, policy enforcement accuracy) and security indicators (e.g., intrusion detection rate, false positives). Results demonstrate that the AI-NFV integration reduces mean time to detect policy breaches by 40%, increases intrusion detection precision by 25%, and ensures end-to-end audit compliance with minimal performance overhead. Furthermore, the model’s elastic deployment capabilities allow banks to reconfigure security and compliance functions on demand, aligning with evolving regulatory landscapes such as GDPR, PSD2, and PCI-DSS. However, integration complexity, AI model explainability, and operational overhead pose adoption challenges. The study concludes that AI-driven NFV can significantly advance data modernization in banking by delivering agile, compliant, and secure infrastructures. Future work should explore real-world pilot deployments, deeper explainability of AI decisions, and scalability across multi-branch environments.