AI-Driven DevOps Automation for Cloud-Native Application Modernization
Akshay Mittal · 2025
Cloud-native architectures demand rapid delivery and operational agility, pushing DevOps practices to their limits. Artificial intelligence (AI) and machine learning (ML) are emerging as key enablers for intelligent automation in DevOps, offering data-driven insights and predictive capabilities. This paper presents a refactored framework for AI-driven DevOps that optimizes the software development lifecycle (SDLC) in cloud-native environments. We explore AI-enhanced continuous integration and delivery (CI/CD), intelligent monitoring (predictive observability), proactive security (DevSecOps), and self-healing infrastructure. AWS, Azure, and GCP case studies illustrate how cloud providers integrate AI/ML to improve deployment automation, anomaly detection, and system resilience. We also incorporate recent advances (2023-2025) in AIOps platforms, large language models for configuration automation, and AI-based cloud security. Finally, we outline future research directions-including explainable and generative AI, federated learning, and ethical AI-charting a course toward sustainable, resilient, and secure cloud application modernization.