Protecting Your Sensitive Data in Azure SQL with Dynamic Data Masking

Raviteja Mureboina
3 min readApr 7, 2023

Dynamic Data Masking (DDM) is a powerful security feature in Microsoft Azure SQL that helps businesses protect sensitive information from unauthorized access. With DDM, businesses can obfuscate sensitive data by masking it, making it more difficult for unauthorized users to access sensitive data.

What is Dynamic Data Masking?

Dynamic Data Masking is a feature in Azure SQL that helps businesses protect sensitive data by masking it. With DDM, sensitive data is replaced with fictional or partially masked values, depending on the user’s access privileges.

DDM can be applied to columns in tables or views that contain sensitive data. The types of masking rules available in Azure SQL Database include:

Partial masking: This replaces part of the sensitive data with asterisks (*) or other characters. For example, a credit card number may be masked to show only the last four digits.

Full masking: This replaces the entire value of the sensitive data with a fictional value. For example, a social security number may be masked to show “XXX-XX-XXXX”.

Random masking: This replaces the sensitive data with a random value that has the same data type and length as the original data.

Why use Dynamic Data Masking?

DDM is an essential security feature in Azure SQL that helps businesses protect their sensitive data. By masking sensitive data, businesses can ensure that only authorized users can access sensitive data. This helps prevent data breaches and unauthorized access to sensitive information.

Dynamic Data Masking is also easy to implement and does not require any changes to the application. The masking rules can be created and applied through the Azure Portal or Transact-SQL commands.

How to use Dynamic Data Masking?

To use Dynamic Data Masking in Azure SQL, you can follow these steps:

Identify the sensitive data: Determine which columns in your database contain sensitive data that needs to be masked.

Create masking rules: Create masking rules that determine how sensitive data is masked. These rules can be applied to columns in tables or views that contain sensitive data.

Test the masking rules: Before…

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Raviteja Mureboina

Hello Everyone, I write blogs on Cybersecurity, ML, and Cloud(AWS, Azure, GCP). please follow to stay updated https://www.youtube.com/c/RaviTejaMureboina