Tutorial: Get started with Jupyter notebooks and MSTICPy in Microsoft Sentinel.MSTIC Jupyter and Python Security Tools documentation.MSTICPy tools are designed specifically to help with creating notebooks for hunting and investigation and we're actively working on new features and improvements. Microsoft Sentinel notebooks use a Python package called MSTICPy, which is a collection of cybersecurity tools for data retrieval, analysis, enrichment, and visualization. Make sure that you import the package, or the relevant part of the package, such as a module, file, function, or class. Azure ML Compute has most common packages pre-installed. To use a package in a notebook, you need to both install and import the package. To avoid having to type or paste complex and repetitive code into notebook cells, most Python notebooks rely on third-party libraries called packages. There are a great many other Python packages for you to choose from, covering areas such as: The Microsoft Sentinel notebooks use many popular Python libraries such as pandas, matplotlib, bokeh, and others. If your notebooks include complex machine learning models, several licensing options exist to use more powerful virtual machines. The VM instance can support running many notebooks at once. The Microsoft Sentinel notebook's kernel runs on an Azure virtual machine (VM). A kernel that is responsible for parsing and executing the code itself.The browser-based interface, where you enter and run queries and code, and where the results of the execution are displayed.Other notebooks may also be imported from the Microsoft Sentinel GitHub repository. Others are intended as samples to illustrate techniques and features that you can copy or adapt for use in your own notebooks.

Some of these notebooks are built for a specific scenario and can be used as-is.Several notebooks, developed by some of Microsoft's security analysts, are packaged with Microsoft Sentinel: The Kqlmagic library provides the glue that lets you take KQL queries from Microsoft Sentinel and run them directly inside a notebook. We've integrated the Jupyter experience into the Azure portal, making it easy for you to create and run notebooks to analyze your data. Integrate data sources outside of Microsoft Sentinel, such as an on-premises data set.Create data visualizations that aren't provided out-of-the box in Microsoft Sentinel, such as custom timelines and process trees.Perform analytics that aren't provided out-of-the box in Microsoft Sentinel, such as some Python machine learning features.While many common tasks can be carried out in the portal, Jupyter extends the scope of what you can do with this data. The same API is also available for external tools such as Jupyter notebooks and Python. The Azure portal and all Microsoft Sentinel tools use a common API to access this data store. The foundation of Microsoft Sentinel is the data store it combines high-performance querying, dynamic schema, and scales to massive data volumes. These attributes make Jupyter a compelling tool for security investigation and hunting. Jupyter notebooks combine full programmability with a huge collection of libraries for machine learning, visualization, and data analysis.

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