Securing Kubernetes: AI-Powered Container Security Agents

Abstract

Kubernetes is now the most popular solution for container orchestration that allows for efficient further cloud native applications deployment. Nonetheless, its dynamic use and complex attack pattern have put traditional rules oriented security measures to offer adequate protection. Container security agents with integrated AI/ML and DL provide a more autonomous, intelligent defense mechanism by detecting anomalies and threats and preventing containers from being compromised by hackers and other unauthorized persons. It first discusses the architecture and implementation of AI-based security in Kubernetes and then examines their efficiency. Specifically, we seek to detail threat detection techniques focusing on behavioral analysis, real-time telemetry, and network traffic analysis of networks with AI models such as Variational Autoencoder (VAE), Convolutional Neural Network (CNN), and Graph Neural Network (GNN) in early and zero-day attack detection and elimination of false positives. Also, we address the strategies for deployment, policy compliance with eBPF and KubeArmor, and compatibility with the other security models. So, based on the evaluation conducted on a 50 node hybrid Kubernetes environment testing, we achieved a 67% faster response than the rule-based approach and achieved 96% less number of false positives. These AI-driven security agents offer runtime protection and automate compliance for related compliance standards such as PCI-DSS and HIPAA. The constant growth of Kubernetes adoption in hybrid cloud and edge computing requires effective security solutions that are intelligent, agile, and sustainable in protecting the containers