AI-driven cloud monitoring: innovations in anomaly detection

Thota, Praveen Kumar (2025) AI-driven cloud monitoring: innovations in anomaly detection. World Journal of Advanced Research and Reviews, 26 (1). pp. 3977-3986. ISSN 2581-9615

This article examines the transformative impact of artificial intelligence on cloud monitoring systems, with a particular focus on innovations in anomaly detection. As cloud architectures grow increasingly complex and distributed, traditional monitoring approaches with static thresholds and manual interventions have proven inadequate. AI-driven systems leverage machine learning algorithms to process massive volumes of telemetry data, identify subtle patterns, and detect anomalies that would otherwise escape human attention. The evolution from reactive to proactive monitoring represents a paradigm shift in cloud observability, enabling organizations to predict and prevent incidents rather than merely respond to them. Through a review of machine learning methodologies, time-series analysis techniques, and real-world applications across multiple industries, the article demonstrates how AI technologies are revolutionizing monitoring practices. These advancements are creating more resilient digital infrastructures capable of self-healing and autonomous operation, fundamentally altering both the economics and reliability of modern cloud environments.