Introducing Agent Safe Governance for the AI Era

Implement Liquibase Secure Custom policy checks

Last updated: July 13, 2026

Liquibase Secure Custom Policy Checks allow you to specify advanced policies using the Liquibase Policy Checks framework. Using Custom Policy Checks, you can enforce compliance for a wide array of security, code standards, data quality, and more.

This collection of the Liquibase Secure Custom Policy Checks is the core set of customized checks that enable you to implement complex governance by confirming specific syntax or objects are present within your Changelog or database, depending on the scope you set. Each check describes what the check can look for and how to implement the check in Liquibase Secure.

Business benefits

  • Ensure compliance with your specific issues and tech stack

  • Develop at your own cadence

  • Share easily across your entire organization

  • Easily customize from a base template

Prerequisites

Liquibase prerequisites

  • Liquibase 4.29.0+

  • Configure a valid Liquibase Secure license key

  • Ensure the Liquibase Checks extension is installed. In Liquibase 4.31.0+, it is already installed in the /liquibase/internal/lib directory, so no action is needed.

  • (Maven users only) Add the liquibase-checks dependency to your pom.xml file. See Add extensions with Maven for more information.

  • Java Development Kit 17+ (available for Open JDK and Oracle JDK)

  • Linux, macOS, or Windows operating system

Testing your Python scripts

Before deploying a custom policy check, test each Python script against both a passing and a failing changeset to confirm it behaves as expected:

  • Passing changeset (positive case): Run the check against a changeset that should pass. Verify that the check does not trigger. If it does, your script logic is producing a false positive and needs to be adjusted.

  • Failing changeset (negative case): Run the check against a changeset that should fail. Verify that the check triggers with the expected severity and message.

Have a test plan for each Python script before deploying it to your pipeline.