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Bytebase is a database DevSecOps platform designed for developers, security, DBA, and platform engineering teams. While it offers an intuitive GUI for managing database schema changes and access control, some teams may want to integrate Bytebase into their existing DevOps platforms using the Bytebase API. In the previous tutorial, you learned how to set up a GitHub Action that utilizes the Bytebase API to define semantic types and global masking rule. In this tutorial, we will explore how to apply column masking and masking exemption.
This is Part 2 of our tutorial series on implementing automated database masking using GitHub Actions:

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Overview

In this tutorial, you’ll learn how to automate column masking and masking exemption using GitHub Actions and the Bytebase API. This integration allows you to:
  • Manage data masking rules as code
  • Automatically apply masking policies when PRs are merged
Here is a merged pull request as an example. The complete code for this tutorial is available at: example-database-security This tutorial skips the setup part, if you haven’t set up the Bytebase and GitHub Action, please follow Setup Instructions section in the previous tutorial.

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Column Masking

Column Masking lets you specify table columns different semantic type to mask the data differently.

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In Bytebase Console

Go to a database page, then pick a table, you can specify semantic type by clicking pen icon on table detail page. bb-column-masking

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In GitHub Workflow

Find the step Apply column masking, which will apply the column masking to the database via API. First it will parse all the column masking files and then do a loop to apply the column masking to the database one by one. The code it calls Bytebase API is as follows: 
response=$(curl -s -w "\n%{http_code}" --request PATCH "${BYTEBASE_API_URL}/instances/${INSTANCE_NAME}/databases/${DATABASE_NAME}/catalog" \
   --header "Authorization: Bearer ${BYTEBASE_TOKEN}" \
   --header "Content-Type: application/json" \
   --data @"$CHANGED_FILE")
By changing file masking/databases/**/**/database-catalog.json, create a PR and then merge, the change will be applied. Log in Bytebase console, at the workspace level, go to the database page, you can see the column semantic type is applied.

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Masking Exemption

Masking Exemption lets you exempt users from data masking and see the unmasked data.

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In Bytebase Console

Go to a project page, then click Manage > Masking Exemptions, you can grant masking exemption to the database. bb-grant-exemption

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In GitHub Workflow

Find the step Apply masking exemption, which applies the masking exemption to the database. The process is similar, and the code to call the Bytebase API is as follows: 
response=$(curl -s -w "\n%{http_code}" --request PATCH "${BYTEBASE_API_URL}/projects/${PROJECT_NAME}/policies/masking_exemption?allowMissing=true&updateMask=payload" \
   --header "Authorization: Bearer ${BYTEBASE_TOKEN}" \
   --header "Content-Type: application/json" \
   --data @"$CHANGED_FILE")
By changing the file masking/projects/**/masking-exemption.json, creating a PR, and merging it, the change will be applied. Log in Bytebase console, go to the project Sample Project, click Manage > Masking Exemptions, you can see the masking exemption is applied.

Next Step: Data Classification