AI-Driven DevOps in Cloud-Native Environments: Opportunities, Architectures, and Challenges

Abstract

The growing complexity of cloud-native systems — built around microservices, containers, and dynamic orchestration platforms like Kubernetes — has stretched traditional DevOps practices to their limits. While CI/CD pipelines, infrastructure as code, and observability tooling have dramatically improved how software gets built and shipped, these approaches still lean heavily on static rules and human judgment that struggle to keep up with the pace and unpredictability of modern distributed environments. This article investigates how artificial intelligence and machine learning techniques can be woven into DevOps workflows to close that gap — a convergence widely known as AIOps. It presents a layered conceptual architecture for AI-enabled DevOps and examines AI applications across three critical operational dimensions: anomaly detection, incident response, and CI/CD pipeline optimization. A synthetic dataset modeled on realistic cloud telemetry was used to evaluate three detection approaches—rule-based thresholds, Random Forest classification, and LSTM-based deep learning. The LSTM model achieved the strongest results with a 94% accuracy rate and an F1-score of 92.5%, outperforming both alternatives by a significant margin. AI-driven incident response cut average resolution time to 10 minutes from the 45 minutes typical of manual workflows, while AI-enhanced pipelines completed delivery cycles roughly 40% faster without sacrificing deployment reliability. Beyond the results, the article candidly addresses persistent challenges around data quality, model interpretability, integration overhead, and adversarial security risks. It concludes by outlining future research paths, including explainable AI, reinforcement learning for adaptive resource management, and the long-term vision of fully autonomous, self-healing DevOps systems.

DOI: https://doi.org/10.17762/ijisae.v14i1s.8212