AI-Driven Kubernetes Cluster Security Posture Management
Keywords:
Kubernetes security, AI-driven insights, real-time monitoring, predictive analyticsAbstract
Container orchestration standard Modern apps can be scaled using Kubernetes. But Kubernetes clusters' dynamic nature makes security difficult. Complex security management makes recognizing and reacting to attacks in big, scattered systems challenging. This study examines AI-based Kubernetes cluster security posture monitoring. Machine learning and predictive analytics help security teams find threats, examine vulnerabilities, and automate repairs. We address AI-based cluster health monitoring, anomaly detection, and security policy compliance. The article discusses AI model integration data quality, interpretability, and system performance. Real Kubernetes examples show AI-driven security. Our results indicate that AI-driven insights aid security teams in Kubernetes management and cloud-native application resilience.
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
