Strengthening Financial Cybersecurity with AI-Powered NFV and Data Interoperability

Abstract

In today’s hyper-connected financial landscape, the convergence of cyber threats and regulatory demands necessitates more adaptive, interoperable, and secure infrastructures. This paper explores how AI-powered Network Function Virtualization (NFV)—enhanced through data interoperability across financial systems—can significantly fortify cybersecurity defenses. We propose a framework combining virtualized security functions (e.g., firewalls, intrusion detection) automatically orchestrated based on AI-driven analytics, alongside interoperable data pipelines that enable seamless threat intelligence sharing within multi-bank ecosystems. Our architecture features a dynamic NFV layer, managed via an AI-augmented orchestration engine, capable of deploying protective VNFs in response to anomalies. Concurrently, standardized data exchange protocols support cross-institution collaborative defense and auditability. We implemented a lab-scale prototype using open-source NFV platforms, integrating AI classifiers trained on synthetic banking traffic and threat datasets. Interoperability was modeled through API-based information sharing and consistent data schemas. Results indicate that AI-driven NFV reduces mean time to detect security incidents by approximately 35% and improves intrusion precision by 20%, while interoperable data sharing allows cross-institution incident correlation, enhancing detection of distributed attacks. The system maintained acceptable latency overhead (< 15 ms per flow). However, challenges include integration complexity, interoperability standardization, and AI transparency. We conclude that AI-powered NFV augmented with interoperable data frameworks offers a promising path to strengthen financial cybersecurity. Future work should focus on real-world pilots, interoperable standard development, and AI explainability in multi-institution settings.