What is Azure Database Dynamic Data Masking?

Azure Database Dynamic Data Masking is a security feature that enables you to mask sensitive data in your database, so that unauthorized users cannot see or access it. With Dynamic Data Masking, you can control the level of access that different users have to your data, without having to modify the underlying data itself.

Usage examples of Azure Database Dynamic Data Masking include:

  1. Hiding credit card numbers: If you have a database that contains credit card numbers, you can use Dynamic Data Masking to mask the numbers so that only authorized users can see the full numbers. This can help prevent unauthorized access to sensitive financial data.
  2. Masking personal information: If your database contains personal information such as social security numbers, addresses, or phone numbers, you can use Dynamic Data Masking to mask the information so that unauthorized users cannot see the full data. This can help protect the privacy of your customers and employees.
  3. Limiting access to data: You can use Dynamic Data Masking to limit the level of access that different users have to your data. For example, you can give read-only access to some users, while masking sensitive data from them, and give full access to other users who need to see the full data.
  4. Complying with regulations: If your organization needs to comply with regulations such as GDPR or HIPAA, you can use Dynamic Data Masking to ensure that sensitive data is protected and that only authorized users have access to it.

To use Dynamic Data Masking in Azure SQL Database, you can use the Azure portal or PowerShell. You can define masking rules for your database columns, specifying which characters to show and which to mask. You can also define different masking rules for different user roles, so that different users see different levels of data masking.

Once Dynamic Data Masking is configured, your data will be automatically masked based on the rules you define, and only authorized users will be able to see the full data.

Author: tonyhughes