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PII detection and masking

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APPLIES TO: Azure Data Factory Azure Synapse Analytics

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This article describes a solution template that you can use to detect and mask PII data in your data flow with Azure AI services.

About this solution template

This template retrieves a dataset from Azure Data Lake Storage Gen2 source. Then, a request body is created with a derived column and an external call transformation calls Azure AI services and masks PII before loading to the destination sink.

The template contains one activity:

  • Data flow to detect and mask PII data

This template defines 3 parameters:

  • sourceFileSystem is the folder path where files are read from the source store. You need to replace the default value with your own folder path.
  • sourceFilePath is the subfolder path where files are read from the source store. You need to replace the default value with your own subfolder path.
  • sourceFileName is the name of the file that you would like to transform. You need to replace the default value with your own file name.

Prerequisites

  • Azure AI services resource endpoint URL and Key (create a new resource here)

How to use this solution template

  1. Go to template PII detection and masking by scrolling through the template gallery or filter for the template.

    Screenshot of template gallery with the PII detection template selected.

  2. Use the drop down to create a New connection to your source storage store or choose an existing connection. The source storage store is where you want to read files from.

    Screenshot of template set up page where you can create a new connection or select an existing connection to the source from a drop down menu.

    Clicking New will require you to create a new linked service connection.

    Screenshot of the template set up page with a fly-out open to create a new linked service connection to a data source.

  3. Use the drop down to create a New connection to your Azure AI services resource or choose an existing connection. You will need an endpoint URL and resource key to create this connection.

    Screenshot of the template set up page to create a new connection or select an existing connection to Azure AI services from a drop down menu.

    Clicking New will require you to create a new linked service connection. Make sure to enter your resource's endpoint URL and the resource key under the Auth header Ocp-Apim-Subscription-Key.

    Screenshot of the template set up page with a fly-out open to create a new linked service connection to Azure AI services.

  4. Select Use this template to create the pipeline.

    Screenshot of button in bottom left corner to finish creating pipeline.

  5. You should see the following pipeline:

    Screenshot of pipeline view with one dataflow activity.

  6. Clicking into the dataflow activity will show the following dataflow:

    Screenshot of the dataflow view with a source leading to three transformations and then a sink.

  7. Turn on Data flow debug.

    Screenshot of the Data flow debug button found in the top banner of the screen.

  8. Update Parameters in Debug Settings and Save.

    Screenshot of the Debug settings button on the top banner of the screen to the right of debug button.

    Screenshot of where to update parameters in Debug settings in a panel on the right side of the screen.

  9. Preview the results in Data Preview.

    Screenshot of dataflow data preview at the bottom of the screen.

  10. When data preview results are as expected, update the Parameters.

    Screenshot of dataflow parameters at the bottom of the screen under Parameters.

  11. Return to pipeline and select Debug. Review results and publish.

    Screenshot of the results that return after the pipeline is triggered.

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