AI-Driven Network Automation in Cloud Environments

Abstract

The integration of artificial intelligence into cloud network automation represents a fundamental transformation in infrastructure management, enabling organizations to transition from manual, error-prone processes to intelligent, self-optimizing systems. This article explores how AI-driven network automation leverages natural language processing, large language models, and multi-region orchestration platforms to revolutionize network deployment, policy management, and operational efficiency in cloud environments. The article examines the convergence of machine learning operations and development operations methodologies, demonstrating how predictive analytics capabilities enhance continuous integration and deployment pipelines while enabling dynamic resource allocation and proactive system optimization. Through comprehensive analysis of implementation architectures, validation frameworks, and operational transformations, this article reveals that AI-powered automation dramatically compresses deployment timelines from weeks to hours, enhances policy lifecycle management through continuous evaluation, and enables self-healing capabilities that detect and remediate issues before impacting service availability. The investigation also addresses critical challenges, including model accuracy assurance, governance framework implementation, and the necessary cultural and skills transformation required for successful adoption. By examining the technical enhancements, validation methodologies, and predictive capabilities of AI-driven systems, this research provides insights into how organizations can harness intelligent automation to achieve substantial improvements in deployment velocity, system reliability, and resource optimization while maintaining appropriate human oversight and risk controls for mission-critical infrastructure operations.