AIOps platforms detect anomalies by combining machine learning, statistical analysis, and correlation across multiple data sources like logs, metrics, and traces, which is especially important in large-scale IT environments where systems are constantly changing; instead of relying on fixed thresholds, these systems first build a baseline of “normal” behavior and then continuously compare incoming data against it to spot deviations. Common techniques include statistical methods like moving averages and standard deviation checks for simpler signals, and more advanced machine learning approaches such as clustering (to group similar behavior patterns), classification models, and time-series forecasting models that predict expected system behavior and flag anything that significantly deviates. In more mature systems, deep learning models like LSTM networks are also used for time-series anomaly detection in highly dynamic environments. To reduce false positives, AIOps tools rely heavily on event correlation, meaning they group related alerts from different systems into a single incident, apply contextual awareness (like deployment changes or maintenance windows), and continuously retrain models to adapt to new patterns. Overall, the combination of baselining, context-aware correlation, and adaptive learning helps AIOps systems distinguish real incidents from noise, making anomaly detection more accurate and actionable in complex modern infrastructures.