Leveraging artificial intelligence for error detection and resolution in data centers as critical infrastructure

Abstract

In a rapidly evolving digital landscape, data centers represent critical components of modern infrastructure, ensuring the continuity of essential digital services. As the volume and complexity of operations increase, the need for rapid and accurate error detection and resolution has become vital for maintaining resilience and reliability. This paper explores how Artificial Intelligence (AI) can enhance these processes, with a focus on custom-coded, Large Language Model (LLM)-based architectures specifically designed for data centers. Unlike standardized or no-code platforms, which offer ease of deployment but limited adaptability, customized AI solutions enable deeper integration with internal systems and domain-specific optimization. The research addresses the question of how AI-driven monitoring and predictive analytics can improve operational efficiency, fault management, and continuity in critical infrastructures. Through the design and experimental evaluation of the Dacosoft AI Monitoring Platform, the study demonstrates the capacity of LLM-powered modules to autonomously detect anomalies, anticipate failures, and recommend remediation actions. Results highlight measurable improvements in accuracy, response time, and scalability compared to conventional monitoring approaches. The findings confirm that intelligent automation based on LLMs strengthens the resilience of data centers by reducing downtime and supporting proactive incident management. Consequently, AI integration emerges not only as a technological advancement but also as a strategic necessity for organizations managing critical infrastructure