Use Jupyter Notebook to hunt for security threats


For information about feature availability in US Government clouds, see the Azure Sentinel tables in Cloud feature availability for US Government customers.

The foundation of Azure Sentinel is the data store; it combines high-performance querying, dynamic schema, and scales to massive data volumes. The Azure portal and all Azure Sentinel tools use a common API to access this data store. The same API is also available for external tools such as Jupyter notebooks and Python. While many common tasks can be carried out in the portal, Jupyter extends the scope of what you can do with this data. It combines full programmability with a huge collection of libraries for machine learning, visualization, and data analysis. These attributes make Jupyter a compelling tool for security investigation and hunting.

example 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. The Kqlmagic library provides the glue that lets you take queries from Azure Sentinel and run them directly inside a notebook. Queries use the Kusto Query Language. Several notebooks, developed by some of Microsoft's security analysts, are packaged with Azure Sentinel. Some of these notebooks are built for a specific scenario and can be used as-is. Others are intended as samples to illustrate techniques and features that you can copy or adapt for use in your own notebooks. Other notebooks may also be imported from the Azure Sentinel Community GitHub.

The integrated Jupyter experience uses Azure Notebooks to store, share, and execute notebooks. You can also run these notebooks locally if you have a Python environment and Jupyter on your computer, or in other JupterHub environments such as Azure Databricks.

Notebooks have two components:

  • The browser-based interface where you enter and run queries and code, and where the results of the execution are displayed.
  • A kernel that is responsible for parsing and executing the code itself.

The Azure Sentinel notebook's kernel runs on an Azure virtual machine (VM). Several licensing options exist to leverage more powerful virtual machines if your notebooks include complex machine learning models.

The Azure Sentinel notebooks use many popular Python libraries such as pandas, matplotlib, bokeh, and others. There are a great many other Python packages for you to choose from, covering areas such as:

  • Visualizations and graphics
  • Data processing and analysis
  • Statistics and numerical computing
  • Machine learning and deep learning

We've also released some open-source Jupyter security tools in a package named msticpy. This package is used in many of the included notebooks. Msticpy tools are designed specifically to help with creating notebooks for hunting and investigation and we're actively working on new features and improvements. For more information, see the MSTIC Jupyter and Python Security Tools documentation.

The Azure Sentinel Community GitHub repository is the location for any future Azure Sentinel notebooks built by Microsoft or contributed from the community.

To use the notebooks, you must first create an Azure Machine Learning (ML) workspace.

Create an Azure ML workspace

  1. From the Azure portal, navigate to Azure Sentinel > Threat management > Notebooks and then select Launch Notebook.

    launch notebook to start azure ml workspace

  2. Under AzureML Workspace, select Create New.

    create workspace

  3. On the Machine Learning page, provide the following information, and then select Review + create.

    Field Description
    Subscription Select the Azure subscription that you want to use.
    Resource group Use an existing resource group in your subscription or enter a name to create a new resource group. A resource group holds related resources for an Azure solution. In this example, we use AzureMLRG.
    Workspace name Enter a unique name that identifies your workspace. In this example, we use testworkspace1. Names must be unique across the resource group. Use a name that's easy to recall and to differentiate from workspaces created by others.
    Region Select the location closest to your users and the data resources to create your workspace.
    Workspace edition Select Basic as the workspace type in this example. The workspace type (Basic & Enterprise) determines the features to which you'll have access and pricing.

    provide workspace details

  4. Review the information, verify that it is correct, and then select Create to start the deployment of your workspace.

    review workspace details

    It can take several minutes to create your workspace in the cloud during which time the Overview page displays the current deployment status.

    workspace deployment

Once your deployment is complete, you can launch notebooks in your new Azure ML workspace.

workspace deployment succeeded

Launch a notebook using your Azure ML workspace

  1. From the Azure portal, navigate to Azure Sentinel > Threat management > Notebooks, where you can see notebooks that Azure Sentinel provides.


    Select Guides & Feedback to open a pane with additional help and guidance on notebooks. view notebook guides

  2. Select individual notebooks to view their descriptions, required data types, and data sources.

    view notebook details

  3. Select the notebook you want to use, and then select Launch Notebook to clone and configure the notebook into a new Azure Notebooks project that connects to your Azure Sentinel workspace. When the process is complete, the notebook opens within Azure Notebooks for you to run.

    select notebook

  4. Under AzureML Workspace, select your Azure ML workspace, and then select Launch.

    launch notebook

  5. Select a compute instance. If you don't have a compute instance, do the following:

    1. Select the plus sign (+) to start the Create compute instance wizard.

      start compute instance wizard

    2. On the Select virtual machine page, provide the required information, and then select Next.

      Select compute instance VM

    3. On the Configure settings page, provide the required information, and then select Create.

      Configure compute instance settings

  6. Once your notebook server is created, within each cell select the run icon to execute code in the notebooks.

    run notebook


  • For a quick introduction to querying data in Azure Sentinel, look at the Getting Started with Azure ML Notebooks and Azure Sentinel guide.

  • You'll find additional sample notebooks in the Sample-Notebooks GitHub subfolder. These sample notebooks have been saved with data, so that it's easier to see the intended output. We recommend viewing these notebooks in nbviewer.

  • The HowTos GitHub subfolder contains notebooks describing, for example: Setting you default Python version, configuring a DSVM, creating Azure Sentinel bookmarks from a notebook, and other subjects.

The notebooks provided are intended as both useful tools and as illustrations and code samples that you can use in the development of your own notebooks.

We welcome feedback, whether suggestions, requests for features, contributed Notebooks, bug reports or improvements and additions to existing notebooks. Go to the Azure Sentinel Community GitHub to create an issue or fork and upload a contribution.

Next steps

In this article, you learned how to get started using Jupyter Notebook in Azure Sentinel. To learn more about Azure Sentinel, see the following articles: