tutorials-and-examples/example-notebooks/Senserva Connections Graph Notebook.ipynb

{
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      "cell_type": "markdown",
      "source": [
        "---\r\n",
        "\r\n",
        "# Welcome to the Senserva Connections Graph Notebook!\r\n",
        "\r\n",
        "<div style=\"color: Black; background-color: Yellow; padding: 5px; font-size: 20px\">\r\n",
        "This notebook serves to visualize connections that exist between Azure Active Directory objects.\r\n",
        "</div>\r\n",
        "<br>\r\n",
        "\r\n",
        "The connections will be an interaction between the objects in the form of membership, ownership, permission, etc. They are \r\n",
        "visualized using a [Graph](https://en.wikipedia.org/wiki/Graph_(discrete_mathematics)). The Graph is an ideal tool to \r\n",
        "visualize as one object can have many different kinds of connections. The nodes of the Graph will be sized according to\r\n",
        "number of connections (more connections = bigger size) and colored according to type of node (Low Risk = Green, High Risk = Red,...).\r\n",
        "\r\n",
        "The data set for the Graph will be fetched from the Log Analytics Workspace that your Senserva Scanner is set up to use.\r\n",
        "More information about that setup process [can be found here](https://www.senserva.com/senserva-setup/).\r\n",
        "\r\n",
        "<br>"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
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    },
    {
      "cell_type": "markdown",
      "source": [
        "## Setup\r\n",
        "\r\n",
        "<div style=\"color: Black; background-color: Yellow; padding: 5px; font-size: 20px\">\r\n",
        "This section will setup our Python imports and Global variables\r\n",
        "</div>\r\n",
        "\r\n",
        "Several processing and visualization libraries are used in this notebook. \r\n",
        "In this notebook we will use the Pyserva Python library that our experts at Senserva have made to visualize your data. \r\n",
        "More information about the library and the source code can be [found on the Github page](https://github.com/Senserva-LLC/Pyserva) "
      ],
      "metadata": {
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      }
    },
    {
      "cell_type": "code",
      "source": [
        "# If you need to know what Python modules are available, you may run this:\r\n",
        "# help(\"modules\")\r\n",
        "\r\n",
        "# Install the Pyserva library\r\n",
        "%pip install Pyserva"
      ],
      "outputs": [],
      "execution_count": null,
      "metadata": {
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    },
    {
      "cell_type": "code",
      "source": [
        "# Load Python libraries that will be used in this notebook to fetch Azure Sentinel data\r\n",
        "from azure.loganalytics.models import QueryBody\r\n",
        "from azure.mgmt.loganalytics import LogAnalyticsManagementClient\r\n",
        "from azure.loganalytics import LogAnalyticsDataClient\r\n",
        "\r\n",
        "# # Processing Helpers\r\n",
        "import Pyserva\r\n",
        "from Pyserva import JupyterNotebookHelper as Senserva\r\n",
        "from Pyserva.StandaloneApiHelper import getTreeTemplate\r\n",
        "from IPython.display import display, HTML, Markdown\r\n",
        "import csv\r\n",
        "import pandas as pd\r\n",
        "import requests\r\n",
        "import numpy as np\r\n",
        "\r\n",
        "# Globals\r\n",
        "# We use a file for data set storage so that we only have to grab once\r\n",
        "defaultFilter = '(no filter applied)'\r\n",
        "filename = 'edge_data.csv'\r\n",
        "ueba_filename = 'ueba_edge_data.csv'\r\n",
        "together_filename = 'together_edge_data.csv'\r\n",
        "dropdown = [defaultFilter]"
      ],
      "outputs": [],
      "execution_count": null,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
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        "gather": {
          "logged": 1640041642999
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      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Calling the above function to populate Sentinel workspace parameters\r\n",
        "# The file, config.json, was generated by the system, however, you may modify the values, or manually set the variables\r\n",
        "tenant_id, subscription_id, resource_group, workspace_id, workspace_name, user_alias, user_object_id = Senserva.read_config_values('config.json');"
      ],
      "outputs": [],
      "execution_count": 3,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
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        "nteract": {
          "transient": {
            "deleting": false
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        },
        "gather": {
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      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Authenticate to Azure\r\n",
        "\r\n",
        "<div style=\"color: Black; background-color: Yellow; padding: 5px; font-size: 20px\">\r\n",
        "This section will authenticate our session for Azure to get data\r\n",
        "</div>\r\n",
        "\r\n",
        "We will use the configs defined above to get data from the Log Analytics workspace. \r\n",
        "The Pyserva library will use the data to analyze and render the data in later steps."
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
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        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# If you need to install MsticPy, run the following line\r\n",
        "# %pip install msticpy\r\n",
        "\r\n",
        "from msticpy.data.data_providers import QueryProvider\r\n",
        "from msticpy.common.wsconfig import WorkspaceConfig\r\n",
        "\r\n",
        "# Use KqlMagic from MsticPy to authenticate to our workspace\r\n",
        "# Note: This will be an interactive login using a device code\r\n",
        "qry_prov = QueryProvider('LogAnalytics')\r\n",
        "wkspace = WorkspaceConfig()\r\n",
        "qry_prov.connect(wkspace.code_connect_str)"
      ],
      "outputs": [],
      "execution_count": null,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
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        },
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    },
    {
      "cell_type": "markdown",
      "source": [
        "## Gather and Process our Data\r\n",
        "\r\n",
        "<div style=\"color: Black; background-color: Yellow; padding: 5px; font-size: 20px\">\r\n",
        "This section will gather the Senserva Scanner data from the Log Analytics Workspace and process it for display\r\n",
        "</div>\r\n",
        "\r\n",
        "We gather the dataset and the Pyserva library will process it for the relevant data points. \r\n",
        "The data points will be saved in a CSV file to your system. \r\n",
        "The data points are stored in the CSV so you can see them and to reduce the amount of web calls.\r\n",
        "A filter dropdown is generated after processing of all objects found. \r\n",
        "Select an object and the visualizations in the following steps will use the dropdown value to filter the data before rendering."
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
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        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# This is the default table name of the Senserva Scanner, but you can change to any alias you may have\r\n",
        "table_name = 'SenservaPro_CL'\r\n",
        "\r\n",
        "kql_query = Senserva.SenservaPermissionQuery(table_name)\r\n",
        "query = \"{0}\".format(kql_query)\r\n",
        "\r\n",
        "# Execute the query\r\n",
        "query_result = qry_prov.exec_query(query)\r\n",
        "\r\n",
        "with open(filename, 'w', newline='') as csvfile:\r\n",
        "    filewriter = csv.writer(csvfile, delimiter=',',\r\n",
        "                        quotechar='|', quoting=csv.QUOTE_MINIMAL)\r\n",
        "\r\n",
        "    # Pluck our data from the Pandas Series\r\n",
        "    # Displays a dropdown menu that allows for selection of node to filter by\r\n",
        "    edge_object_dropdown = Senserva.PluckDataFromQueryResults(query_result, dropdown, filewriter)    \r\n",
        "    "
      ],
      "outputs": [],
      "execution_count": null,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
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        "gather": {
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    },
    {
      "cell_type": "code",
      "source": [
        "ueba_edges = []\r\n",
        "with open(filename, newline='') as csvfile:\r\n",
        "    file_df = pd.read_csv(csvfile,delimiter=',')\r\n",
        "    \r\n",
        "    ueba_kql_query = Senserva.SenservaPermissionUebaQuery()\r\n",
        "    ueba_query = \"{0}\".format(ueba_kql_query)\r\n",
        "\r\n",
        "    # Execute the query\r\n",
        "    ueba_result = qry_prov.exec_query(ueba_query)\r\n",
        "\r\n",
        "    for index, row in file_df.iterrows():\r\n",
        "        if(row['SourceId'] is not np.nan):\r\n",
        "            for value in ueba_result.iterrows():\r\n",
        "                source_id = value[1][0]\r\n",
        "                if(row['SourceId'] == source_id):\r\n",
        "                    rbac_name = value[1][1]\r\n",
        "                    rbac_id = value[1][2]\r\n",
        "                    rbac_object = value[1][3]\r\n",
        "                    rbac_role = value[1][4]\r\n",
        "                    relation = \"{0} {1}\".format(rbac_object, rbac_role)\r\n",
        "                    ueba_edges.append([rbac_name, rbac_id, rbac_object, row['Source'], source_id, row['SourceType'], row['SourceWeight'], row['SourceWeight'], relation, relation, row['Risk'], row['UserMail'], row['UserManagerMail']])\r\n",
        "\r\n",
        "with open(ueba_filename, 'w', newline='') as csvfile:\r\n",
        "    filewriter = csv.writer(csvfile, delimiter=',',\r\n",
        "                        quotechar='|', quoting=csv.QUOTE_MINIMAL)\r\n",
        "            \r\n",
        "    filewriter.writerow(['Source', 'SourceId', 'SourceType', 'Target', 'TargetId', 'TargetType', 'SourceWeight', 'TargetWeight', 'Relationship', 'Reason', 'Risk', 'UserMail', 'UserManagerMail'])\r\n",
        "    for edge in ueba_edges:\r\n",
        "        filewriter.writerow(edge)"
      ],
      "outputs": [],
      "execution_count": null,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
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    },
    {
      "cell_type": "markdown",
      "source": [
        "## Gather, Process, and Visualize the Data\r\n",
        "\r\n",
        "<div style=\"color: Black; background-color: Yellow; padding: 5px; font-size: 20px\">\r\n",
        "This section will use the gathered data and render it in several ways\r\n",
        "</div>\r\n",
        "\r\n",
        "Data will be visualized as:\r\n",
        "- A Graph\r\n",
        "- A Tree\r\n",
        "\r\n",
        "Data will be classified\r\n",
        "- Active Directory (AD)\r\n",
        "- Role Based Access Control (RBAC)\r\n",
        "- Together (AD + RBAC)\r\n",
        "\r\n",
        "If you change the filter in the above dropdown, the visualizations will need to be rerun to take effect"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Active Directory (AD)"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Key Features of the Table\r\n",
        "- Risk - A score representing the suspicion of an object\r\n",
        "- Weight - Impact an object can have on a network\r\n",
        "- Overall - Using the Risk and Weight, a score to indicate what objects are most likely to be compromised"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "items = []\r\n",
        "\r\n",
        "with open(filename, newline='') as csvfile:\r\n",
        "    file_df = pd.read_csv(csvfile,delimiter=',')\r\n",
        "    \r\n",
        "    # Render our data as a Graph\r\n",
        "    # The graph will apply a filter from the previous cell's\r\n",
        "    # dropdown value. If a new filter needs to be applied,\r\n",
        "    # select it from the dropdown and then re-run this cell\r\n",
        "    items = Senserva.filterDataFrameAndCreateList(edge_object_dropdown, file_df, defaultFilter)\r\n",
        "\r\n",
        "\r\n",
        "HTML(getTreeTemplate().format(inputData = Senserva.htmlTreeParser(items)))"
      ],
      "outputs": [],
      "execution_count": null,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1640094326882
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    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Key Features of the Graph\r\n",
        "- Color - Represents the risk of an object, green/yellow/red\r\n",
        "- Size - Represents the number of connections, bigger size = more exposure\r\n",
        "- Shape - Represents the type of object, see legend\r\n",
        "- Relationships - Represents what interactions an object has the network\r\n",
        "\r\n",
        "###### Legend\r\n",
        "- ○ - User\r\n",
        "- ◊ - Group\r\n",
        "- △ - Application \r\n",
        "- ☆ - Service Principal\r\n",
        "- ➕ - Disabled User\r\n",
        "- □ - Role\r\n",
        "- ⬡ - PIM Role\r\n",
        "- ⦻ - Default"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "with open(filename, newline='') as csvfile:\r\n",
        "    file_df = pd.read_csv(csvfile,delimiter=',')\r\n",
        "    \r\n",
        "    # Render our data as a Graph\r\n",
        "    # The graph will apply a filter from the previous cell's\r\n",
        "    # dropdown value. If a new filter needs to be applied,\r\n",
        "    # select it from the dropdown and then re-run this cell\r\n",
        "    graph = Senserva.RenderGraphData(Senserva.filterHelper(edge_object_dropdown, file_df, defaultFilter))\r\n",
        "    Senserva.show(graph)\r\n",
        "        "
      ],
      "outputs": [],
      "execution_count": null,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1640094332013
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      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Role Based Access Control (RBAC)"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Key Features of the Table\r\n",
        "- Risk - A score representing the suspicion of an object\r\n",
        "- Weight - Impact an object can have on a network\r\n",
        "- Overall - Using the Risk and Weight, a score to indicate what objects are most likely to be compromised"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "items = []\r\n",
        "\r\n",
        "with open(ueba_filename, newline='') as csvfile:\r\n",
        "    file_df = pd.read_csv(csvfile,delimiter=',')\r\n",
        "    \r\n",
        "    # Render our data as a Graph\r\n",
        "    # The graph will apply a filter from the previous cell's\r\n",
        "    # dropdown value. If a new filter needs to be applied,\r\n",
        "    # select it from the dropdown and then re-run this cell\r\n",
        "    items = Senserva.filterDataFrameAndCreateList(edge_object_dropdown, file_df, defaultFilter)\r\n",
        "\r\n",
        "HTML(getTreeTemplate().format(inputData = Senserva.htmlTreeParser(items)))"
      ],
      "outputs": [],
      "execution_count": null,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1640094336881
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Key Features of the Graph\r\n",
        "- Color - Represents the risk of an object, green/yellow/red\r\n",
        "- Size - Represents the number of connections, bigger size = more exposure\r\n",
        "- Shape - Represents the type of object, see legend\r\n",
        "- Relationships - Represents what interactions an object has the network\r\n",
        "\r\n",
        "###### Legend\r\n",
        "- ○ - User\r\n",
        "- ◊ - Group\r\n",
        "- △ - Application \r\n",
        "- ☆ - Service Principal\r\n",
        "- ➕ - Disabled User\r\n",
        "- □ - Role\r\n",
        "- ⬡ - PIM Role\r\n",
        "- ⦻ - Default"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "with open(ueba_filename, newline='') as csvfile:\r\n",
        "    file_df = pd.read_csv(csvfile,delimiter=',')\r\n",
        "    \r\n",
        "    # Render our data as a Graph\r\n",
        "    # The graph will apply a filter from the previous cell's\r\n",
        "    # dropdown value. If a new filter needs to be applied,\r\n",
        "    # select it from the dropdown and then re-run this cell\r\n",
        "    ueba_graph = Senserva.RenderGraphData(Senserva.filterHelper(edge_object_dropdown, file_df, defaultFilter))\r\n",
        "    Senserva.show(ueba_graph)"
      ],
      "outputs": [],
      "execution_count": null,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1640094340032
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Together (AD + RBAC)"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Key Features of the Table\r\n",
        "- Risk - A score representing the suspicion of an object\r\n",
        "- Weight - Impact an object can have on a network\r\n",
        "- Overall - Using the Risk and Weight, a score to indicate what objects are most likely to be compromised"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "items = []\r\n",
        "ad_file_df = []\r\n",
        "ueba_file_df = []\r\n",
        "\r\n",
        "with open(filename, newline='') as csvfile:\r\n",
        "    ad_file_df = pd.read_csv(csvfile,delimiter=',')\r\n",
        "with open(ueba_filename, newline='') as csvfile:\r\n",
        "    ueba_file_df = pd.read_csv(csvfile,delimiter=',')\r\n",
        "\r\n",
        "together_file_df = ad_file_df.append(ueba_file_df, ignore_index=True)\r\n",
        "items = Senserva.filterDataFrameAndCreateList(edge_object_dropdown, together_file_df, defaultFilter)\r\n",
        "HTML(getTreeTemplate().format(inputData = Senserva.htmlTreeParser(items)))"
      ],
      "outputs": [],
      "execution_count": null,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1640094344054
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Key Features of the Graph\r\n",
        "- Color - Represents the risk of an object, green/yellow/red\r\n",
        "- Size - Represents the number of connections, bigger size = more exposure\r\n",
        "- Shape - Represents the type of object, see legend\r\n",
        "- Relationships - Represents what interactions an object has the network\r\n",
        "\r\n",
        "###### Legend\r\n",
        "- ○ - User\r\n",
        "- ◊ - Group\r\n",
        "- △ - Application \r\n",
        "- ☆ - Service Principal\r\n",
        "- ➕ - Disabled User\r\n",
        "- □ - Role\r\n",
        "- ⬡ - PIM Role\r\n",
        "- ⦻ - Default"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "together_graph = Senserva.RenderGraphData(Senserva.filterHelper(edge_object_dropdown, together_file_df, defaultFilter))\r\n",
        "Senserva.show(together_graph)"
      ],
      "outputs": [],
      "execution_count": null,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1640094347850
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      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Wrap-Up\r\n",
        "\r\n",
        "Seeing the connections of your Azure Active Directory objects helps give an encompassing view not currently available through Azure.\r\n",
        "Using this information, you can more effectively investigate how these objects interact with each other.\r\n",
        "These visuals save time in how a huge network like Azure Active Directory is used. "
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              "value": "\nSelect a provider to edit its settings.<br>\nTo add a new provider, select the name from the \"Add prov\" drop-down and click \"Add\"<br>\n\nThe Storage option tells the settings module to look for the\nvalue in one of three places:\n<ul>\n    <li>Text - this is the usual way to store values that are not sensitive</li>\n    <li>Environment Var - Type the name of variable in the text box</li>\n    <li>Key Vault - use this for sensitive data like passwords and API keys<br>\n    The KeyVault value here can be left empty (the secret name is generated\n    from the settings path), can contain a secret name or can contain a\n    path {vaultname}/{secretname}\n    </li>\n</ul>\n\n<b>Note:</b> Storing values in KeyVault requires some work on your part<br>\nYou must do the following:\n<ol>\n    <li>Create the Key Vault</li>\n    <li>Add the settings for the Vault in the KeyVault section of the configuration</li>\n    <li>Add the values that you want to use to the Vault</li>\n</ol>\nThe Key Vault Configuration link below describes this setup and how you can\nconfigure your Key Vault settings and transfer secrets settings from your\nconfiguration file to a vault.\n<br><a href='https://msticpy.readthedocs.io/en/latest/data_acquisition/TIProviders.html#configuration-file' target='_blank' style='color: blue; text-decoration: underline;'>Online: Threat Intel Providers</a><br><a href='https://msticpy.readthedocs.io/en/latest/getting_started/msticpyconfig.html#specifying-secrets-as-key-vault-secrets' target='_blank' style='color: blue; text-decoration: underline;'>Online: Key Vault Configuration</a><br><a href='https://msticpy.readthedocs.io/en/latest/getting_started/msticpyconfig.html' target='_blank' style='color: blue; text-decoration: underline;'>Online: MSTICPy Configuration</a><br><a href='https://msticpy.readthedocs.io/en/latest/getting_started/SettingsEditor.html#adding-threat-intelligence-ti-providers' target='_blank' style='color: blue; text-decoration: underline;'>Online: Help on this tab</a>"
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