Authors: Ethan Cole Harrison, Prof. Daniel Reeves Walker, Prof. Emily Carter Hayes, Dr. Christopher Liam Foster, Naveen Kumar
Abstract: Infrastructure as Code (IaC) has emerged as a foundational paradigm for automating cloud infrastructure provisioning, configuration management, and deployment orchestration across modern enterprise environments. However, the growing complexity of multi-cloud architectures, dynamic scaling requirements, and heterogeneous deployment policies has increased the difficulty of maintaining reliable and secure declarative infrastructure templates. This research explores the integration of Generative Artificial Intelligence and neural modeling techniques into Infrastructure as Code workflows to enable intelligent, adaptive, and automated cloud infrastructure engineering. The proposed framework leverages large language models, transformer-based neural architectures, and AI-assisted configuration synthesis to generate, validate, optimize, and remediate declarative infrastructure definitions across cloud platforms. The study investigates how generative models can enhance infrastructure provisioning accuracy, reduce manual scripting complexity, improve deployment consistency, and accelerate DevOps and platform engineering operations. Furthermore, the research examines AI-driven policy validation, anomaly detection, infrastructure drift correction, security compliance automation, and predictive resource optimization within declarative cloud ecosystems. Experimental analysis demonstrates that neural-assisted IaC generation significantly improves deployment efficiency, operational scalability, infrastructure resilience, and automation intelligence while minimizing configuration errors and deployment failures. The findings highlight the transformative potential of generative AI in enabling autonomous cloud operations, intelligent infrastructure orchestration, and next-generation cloud-native automation frameworks for enterprise-scale digital transformation initiatives.
DOI: http://doi.org/10.5281/zenodo.20351381
