Customizable machine learning (ML) based anomalies for Azure Sentinel are now available for public preview. Security analysts can use anomalies to reduce investigation and hunting time as well as improve their detections. Typically, these benefits come at the cost of a high benign positive rate, but Azure Sentinel’s customizable anomaly models are tuned by our data science team and trained with the data in your Sentinel workspace to minimize the benign positive rate, providing out-of-the box value. If security analysts need to tune them further, however, the process is simple and requires no knowledge of machine learning. In this blog, we will discusswhat is an anomaly rule ,what the results generated by the anomaly rules look like ,how to customize those anomaly rules, andthe typical use cases of anomalies.