Anomaly Detection

Anomaly detection is the technology by which AI automatically identifies patterns or behaviors that deviate from the norm, flagging them as anomalies, outliers, or early warning signals. By learning what "normal" looks like from large volumes of data, it detects deviations that indicate potential problems or risks before they escalate. Commonly used approaches for anomaly detection include: • Statistical methods (outlier detection) • Machine learning-based clustering and classification models • Deep learning models for time-series data prediction • Autonomous pattern recognition using unsupervised learning Primary use cases: • Server and network failure detection • Predictive maintenance for manufacturing equipment (detecting early signs of failure) • Credit card fraud detection • Security incident monitoring from log data • Detection of abnormal physiological responses in health monitoring Anomaly detection is an AI application domain that helps reduce human error and build rapid response capabilities, and has seen widespread adoption across manufacturing, finance, healthcare, and IT infrastructure.