AegisAI: An AI-Driven Predictive Health Monitoring Framework for Windows Server Infrastructure

This paper presented AegisAI, an Artificial Intelligence-driven predictive health monitoring framework for Windows Server infrastructure. The proposed framework addresses the limitations of conventional reactive monitoring systems by integrating continuous infrastructure monitoring, predictive analytics, health assessment, adaptive risk prioritization, and intelligent decision support into a unified architecture. AegisAI introduces four key components: the AegisAI Health Index (AHI), Predictive Intelligence Engine (PIE), Adaptive Risk Prioritization (ARP), and Predictive Infrastructure Risk Analysis (PIRA). Together, these components enable administrators to monitor infrastructure health, predict potential failures, prioritize operational risks, and perform preventive maintenance more effectively. The modular architecture, centralized dashboard, and AI-assisted predictive capabilities demonstrate the potential of intelligent infrastructure monitoring for enterprise Windows Server environments. By shifting from reactive monitoring to proactive infrastructure management, the proposed framework contributes to improved system reliability, reduced downtime, optimized resource utilization, and enhanced operational efficiency. Future enhancements involving cloud-native monitoring, advanced machine learning techniques, and automated remediation have the potential to further strengthen the framework and expand its applicability across modern enterprise infrastructures.

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