Security Analysts deal with extremely large datasets in Azure Sentinel, making it challenging to efficiently analyze them for anomalous data points. We sought to streamline the data analysis process by developing a notebook based toolset to reduce the data to a more manageable format, effectively allowing analysts to easily and efficiently gain a better understanding of their dataset and detect anomalies therein. Our toolset has three main components that each provide a different way of turning raw data into useful insights: data inference, feature engineering, and anomaly detection.
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