A Multi-Task Neural Framework for Unified Alert Processing and Incident Prediction in Enterprise IT Systems

Effective incident management in modern IT systems requires timely interpretation and routing of alerts generated from diverse sources such as SNMP Traps, Syslog messages, and xMatters notifications. However, conventional frameworks often lack unified processing and intelligent automation, resulting in delayed response and SLA violations. This paper presents an AI-enhanced unified alerting and incident management framework that integrates heterogeneous alert streams via the ServiceNow platform. Leveraging two real-world datasets comprising over 140,000 event records and 24,000 unique incidents, we implement a multi-task deep neural network to jointly predict resolution time, incident priority, and responsible assignment group. The proposed method incorporates temporal feature engineering, trainable embeddings for categorical data, and variational autoencoders for dimensionality reduction. A synthetic alert-source simulation is introduced to mimic real-world alert diversity within the data pipeline. Experimental results demonstrate superior performance over baseline models in all key metrics, validating the effectiveness of the proposed architecture. The framework sets the stage for scalable, automated, and context-aware incident triaging in enterprise IT environments.