AI-Driven Real-Time Anomaly Detection and Adaptive Response Framework for Kubernetes Security

Abstract

The rapid proliferation of Kubernetes (K8s) as the standard for container orchestration has fundamentally altered the cloud-native security landscape, shifting the defensive focus from static perimeters to dynamic, micro-segmentation requirements. Conventional security tools, which often rely on signature-based detection and manual human-in-the-loop interventions, struggle to keep pace with the ephemeral nature of microservices and the velocity of modern automated attacks. Consequently, organizations face a critical "remediation gap" where the time required to detect and manually respond to a breach allows threat actors ample opportunity to move laterally, escalate privileges, and exfiltrate sensitive data. To address this critical vulnerability, this paper introduces an "Auto-Immune" security framework that converges deep kernel-level telemetry with autonomous artificial intelligence. By leveraging Extended Berkeley Packet Filter (eBPF) sensors for high-fidelity data ingestion and unsupervised machine learning models for continuous behavioral baselining, the system creates a closed-loop observation and decision engine. The findings demonstrate that this framework successfully executes granular, policy-based mitigations in sub-second timeframes, significantly reducing the Mean Time to Respond (MTTR) while maintaining a near-zero false-positive rate, thereby providing a resilient, highly scalable security posture for enterprise Kubernetes environments.