Hybrid AI-SDN Framework for Adaptive Zero Trust Security with Real-Time Intrusion Response

Abstract

The rise of dynamic, heterogeneous, and large-scale networks has amplified the demand for advanced security paradigms capable of addressing evolving cyber threats. Software-Defined Networking (SDN) has emerged as a critical enabler of programmability and centralized control, while Zero Trust Security (ZTS) provides a principle-driven approach to continuous verification and least-privilege access. However, the increasing sophistication of adversarial attacks necessitates integrating Artificial Intelligence (AI) into SDN to achieve adaptive and real-time defense. This paper proposes a Hybrid AI-SDN framework for adaptive Zero Trust security with real-time intrusion response. The framework leverages AI-driven anomaly detection, dynamic trust evaluation, and predictive analytics integrated with the programmability of SDN controllers to orchestrate immediate, multi-layered defensive responses. It emphasizes automated policy adaptation, proactive containment, and distributed enforcement to minimize attack impact. By combining the agility of SDN, the intelligence of AI, and the principles of Zero Trust, the hybrid framework addresses key challenges such as scalability, adversarial robustness, and interoperability across multi-tenant cloud environments. The discussion highlights the architectural design, operational workflow, and potential research avenues toward building autonomous, resilient, and explainable security infrastructures.