1. Anomaly Detection with Azure Sentinel Notebooks


    To set up anomaly detection with Azure Sentinel Notebooks, you'll want to integrate various services offered by Azure, such as Azure Sentinel for security information and event management (SIEM) and Azure Notebooks for running Jupyter notebooks that can analyze and visualize data.

    Within Azure Sentinel, you can create custom analytic rules that can trigger incidents based on the anomalies detected in your log data. You'll then be able to use Azure Notebooks to investigate these incidents further and build machine learning models.

    Below is a Python program using Pulumi to set up Azure resources that could assist with anomaly detection. The program will define the creation of an Azure Log Analytics workspace and enable Azure Sentinel on it. Note that setting up the actual Jupyter notebooks and logic for anomaly detection analysis isn’t done via Pulumi, as Jupyter notebooks are operational tools that you would author and run within Azure Notebooks or another environment capable of hosting Jupyter notebooks.

    Pulumi Program for Azure Sentinel Setup

    import pulumi from pulumi_azure_native import insights from pulumi_azure_native import securityinsights as sentinel # Configuration variables for the Azure resources. resource_group_name = 'rg-sentinel' workspace_name = 'workspace-sentinel' log_analytics_workspace_name = 'la-sentinel' # Create an Azure resource group resource_group = insights.ResourceGroup("resourceGroup", resource_group_name=resource_group_name) # Create an Azure Log Analytics workspace log_analytics_workspace = insights.Workspaces("logAnalyticsWorkspace", resource_group_name=resource_group.name, workspace_name=log_analytics_workspace_name, sku=insights.WorkspaceSkuArgs( name="PerGB2018" # The pricing tier of the workspace ) ) # Enable Azure Sentinel sentinel_workspace = sentinel.Workspace("sentinelWorkspace", workspace_name=log_analytics_workspace.name, resource_group_name=resource_group.name) # Export the IDs of the created resources. pulumi.export('resource_group_id', resource_group.id) pulumi.export('log_analytics_workspace_id', log_analytics_workspace.id) pulumi.export('sentinel_workspace_id', sentinel_workspace.id)

    In the above program:

    • We start by importing pulumi and the necessary Azure-native Pulumi providers, which are used to interact with Azure resources.
    • The insights.ResourceGroup resource class is used to create a new Azure Resource Group, which is a container that holds related resources for an Azure solution.
    • The insights.Workspaces resource class creates an Azure Log Analytics workspace, which is where Azure Sentinel data will be aggregated and analyzed.
    • The sentinel.Workspace resource class enables Azure Sentinel for the created Log Analytics workspace.
    • We end the program by exporting the IDs of created resources using pulumi.export. These can be used for referencing the resources in future operations or in other Pulumi stacks.

    This program demonstrates the infrastructure basics to get you started with anomaly detection using Azure Sentinel. The next step after setting up Azure Sentinel would be to configure the analytic rules, and this typically involves specifying the criteria for detecting anomalies and the actions to take when such anomalies are detected.

    Please be aware that anomaly detection is an advanced topic, typically requiring knowledge of data science and security. Once the Pulumi program runs and the Azure infrastructure is deployed, you would continue by setting up the analytic rules directly in the Azure portal or by using additional tooling to author the Jupyter notebooks for in-depth analysis.