AI-Assisted Network Virtualization for Privacy Preserving Financial Data Modernization

Abstract

In an era of increasingly stringent data privacy regulations and evolving cyber threats, financial institutions are turning toward data modernization enabled by AI-assisted network virtualization. This paper introduces a novel framework that integrates virtualized network functions (VNFs) with AI-driven orchestration to modernize financial data handling while preserving privacy. The architecture leverages dynamic network segmentation, encrypted communication channels, and privacy-aware routing, all managed by AI algorithms that adapt deployment based on data sensitivity, threat levels, and compliance requirements. We developed a prototype using an NFV infrastructure, employing virtual functions like secure micro-segmentation, tokenization gateways, and behavioral analytics modules. AI agents—trained via federated learning to avoid centralized privacy risks—monitor traffic flows and orchestrate virtual functions, ensuring sensitive data stays within secure zones. We tested the system in a simulated banking environment encompassing anonymized transaction datasets and synthetic threat vectors. Performance evaluation reveals that the framework reduces unauthorized data exposure by 45%, enforces segmented and tokenized flows with 98% accuracy, and maintains compliance audit readiness (e.g., GDPR, PCI-DSS) while introducing minimal latency overhead (~12 ms per transaction). Federated AI reduced centralized data sharing by over 60%, bolstering privacy. Key challenges include federated model convergence speed, orchestration complexity, and the balance between privacy and performance. Our findings demonstrate that AI-assisted network virtualization, when combined with privacy-preserving mechanisms, delivers secure, compliant, and flexible modernization of financial data infrastructure. Future research should aim at real-world deployments, multi-tenant scalability, and explainable federated AI in privacy contexts.