AUTONOMOUS POLICY DRIFT DETECTION IN CDN-WAF PIPELINES USING GRAPH NEURAL NETWORKS

Abstract

The increasing complexity of web-based attacks and the dynamic nature of web traffic trends are placing significant burdens on the effectiveness of security policy provisions within Content Delivery Network (CDN) and Web Application Firewall (WAF) pipelines. As adversarial strategies continue to evolve, traditionally fixed sets of detection rules are becoming obsolete. This leads to policy drift, wherein existing detection systems lose their efficacy. This review paper aims to analyze the application of autonomous drift detection using Graph Neural Networks (GNNs) and reinforcement learning-based frameworks as a solution to this challenge. It explores the relevance of adaptive control systems, actor-critic architectures, anomaly detection techniques, and governance-aware AI deployments, particularly in the context of their integration into CDN-WAF infrastructures. The paper highlights how GNNs can capture intricate structural patterns within web traffic data, how continuous security policies can be dynamically learned using deep reinforcement learning, and how responsible AI practices can ensure compliance and transparency. Furthermore, it provides an overview of current trends, defines key architectural components, and discusses the challenges associated with deploying autonomous mechanisms for detecting policy drift in high-security environments.