Strategies for Reducing Alert Fatigue and Improving Signal Quality in Enterprise Monitoring Architectures

( Vol-12,Issue-3,May - June 2026 )

Author(s): Sushrutha Sreevathsa

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Page No: 444-451
DOI: 10.22161/ijaems.123.42

Keywords:

Artificial Intelligence for IT Operations (AIOps), alert aggregation, alert fatigue, enterprise monitoring, signal quality

Abstract:

Enterprise monitoring architectures now generate dense alert streams, yet many notifications fail to support fast incident handling. The article examines alert fatigue as an architectural and operational failure caused by redundant thresholds, fragmented telemetry, weak incident correlation, and shallow prioritization rules. The aim is to develop an analytical model for improving signal quality without using proprietary operational data. The study draws on recent academic literature covering AIOps, alert aggregation, dynamic suppression, observability, log anomaly detection, and incident management. Comparative source analysis, typologization, and conceptual synthesis distinguish rule-based, correlation-based, statistical, and machine learning-supported strategies. The results identify three intervention layers: monitor design, alert lifecycle management, and decision-oriented incident routing. The discussion develops a practical model that links suppression, aggregation, enrichment, and ticket prioritization into a staged operating sequence. The article offers guidance for enterprise teams seeking fewer alerts, stronger diagnostic value, and more reliable escalation.

Received: 25 May 2026; Received in revised form: 21 Jun 2026; Accepted: 25 Jun 2026; Available online: 28 Jun 2026

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