AI-AUGMENTED GitOps FOR on-prem RED HAT KUBERNETES: A FRAMEWORK FOR PREDICTIVE DEPLOYMENT RISK ANALYSIS AND ROLLBACK OPTIMIZATION

Abstract The growing complexity of current software implementation, especially on-premise Red Hat Kubernetes systems, requires more sophisticated approaches to risk management in the deployment and optimization of rollback processes. This paper presents a GitOps architecture embedded with AI capabilities that provides better operational efficiency and system reliability in such a dynamic infrastructure. Based on the concept of GitOps to define declarative configuration and version control, the framework deploys artificial intelligence and machine learning methods to deliver predictive analysis of deployment risks. The main elements are intelligent anomaly detection and predictive maintenance as a means to proactively detect any potential problem and automate rollback plans to reduce downtime and avoid misconfigurations. This solution can be used to offer a solid solution for handling and automating Kubernetes deployments in advanced on-premises environments by moving toward proactive operations.