AI-Enhanced Analytics for Mission-Critical Communication Infrastructure and Incident Analysis

This study examines the application of artificial intelligence (AI)-enhanced analytics to mission-critical communication infrastructure and incident analysis systems. Mission-critical communication environments require high levels of reliability, availability, rapid response, and operational continuity, making conventional monitoring and incident investigation approaches increasingly insufficient for managing large volumes of heterogeneous and rapidly generated data. The proposed approach integrates AI-driven data analytics, anomaly detection, predictive modeling, and automated incident classification to improve the identification and analysis of operational abnormalities. The study focuses on developing an intelligent analytical framework capable of processing communication logs, network performance indicators, system events, and incident-related data to detect emerging failures and support timely decision-making. Machine learning techniques can be employed to identify abnormal behavioral patterns, estimate potential system disruptions, correlate multiple incident indicators, and prioritize critical events. The framework also supports the transition from reactive incident response toward predictive and proactive infrastructure management. Particular attention is given to data quality, model accuracy, real-time processing, explainability, cybersecurity, and the reliability requirements of mission-critical environments. The expected results demonstrate that AI-enhanced analytics can strengthen situational awareness, reduce incident detection and investigation time, improve resource allocation, and support more resilient communication infrastructure. The proposed framework provides a methodological basis for integrating intelligent analytics into mission-critical communication and incident management processes.