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Dynamic data masking (DDM) in Azure SQL Database, Azure SQL Managed Instance, and SQL Server is a security feature that hides sensitive data in the result set of a query. You can have a central control over your sensitive data without modifying your applications.
The first step is to identify the sensitive data that needs to be masked. Common examples include:
Once the sensitive data has been identified, masking rules can be applied directly on the database fields that need protection. Here are a few common types of masks:
After the masking rules are defined, it’s crucial to manage permissions. SQL Database and SQL Managed Instance use standard SQL security for granting permissions to the database.
GRANT UNMASK TO [User/Role];
REVOKE UNMASK TO [User/Role];
db_owner
and db_datawriter
roles can see unmasked data by default.Consider a table named Customer
with a column EmailAddress
. To mask the email addresses, we could apply the following rule:
ALTER TABLE Customer
ADD MASKED WITH (FUNCTION = ’email()’) FOR EmailAddress;
This will mask the EmailAddress
column in a way that a user will see j
instead of [email protected]
, maintaining the first character and the domain structure.
To configure DDM via the Azure Portal, follow these steps:
sys.masked_columns
view to see the masked columns.Dynamic data masking is a crucial security feature for protecting sensitive data in SQL workloads on Microsoft Azure. It provides an easy-to-configure layer of data protection that doesn’t require changes to the application code. Security professionals, especially those preparing for the AZ-500 exam, should understand how to implement and manage dynamic data masking to ensure the security and compliance of their Azure SQL databases.
Correct Answer: True
Dynamic Data Masking is a feature supported on Azure SQL Database, Azure SQL Managed Instance, and SQL Server hosted on Azure VMs.
Correct Answer: False
Dynamic data masks can be altered or dropped after they have been created to adjust the mask or remove it entirely.
Correct Answer: A, B, D
Default full masking, custom string masking, and partial masking are all types of dynamic data masking. Random masking is not a predefined masking function for SQL workloads.
Correct Answer: True
Configuring dynamic data masking requires control permissions on the database to create, alter, or drop the masking rules.
Correct Answer: D
By default, only users with the ‘UNMASK’ permission have the ability to view unmasked data. The other roles and permissions listed do not provide the ability to see unmasked data unless explicitly granted.
Correct Answer: False
Dynamic data masking is not a substitute for proper access control and encryption. It is an additional layer that helps prevent unauthorized users from viewing sensitive data during querying.
Correct Answer: A
The ‘ALTER TABLE’ SQL statement, along with appropriate dynamic data masking functions, is used to add a mask to a column.
Correct Answer: D
To remove a dynamic data mask, the ‘ALTER TABLE’ statement is used with ‘ALTER COLUMN’ to specify the column name, followed by ‘DROP MASKED’.
Correct Answer: False
Users can use a default function such as ‘default()’ to mask the entire field with a predefined character (such as “XXXX” or “0” for numeric fields).
Correct Answer: A
The Azure Contributor role has sufficient permissions to manage resources, including configuring dynamic data masking policies.
Correct Answer: True
Dynamic data masking can be configured through multiple methods, including the Azure portal, T-SQL commands, and PowerShell cmdlets, providing flexibility in management.
Correct Answer: C
The ’email()’ function in dynamic data masking masks the email address except for the first letter of the email username and the domain portion after the “@” sign.
Dynamic data masking is a feature that enables you to restrict sensitive data from being viewed by unauthorized users. It is achieved by hiding the sensitive data from the user or application that is accessing the data, while still allowing the user or application to perform operations on the masked data.
Some examples of sensitive data types that could be masked using dynamic data masking include personally identifiable information (PII), financial data, or other types of sensitive data.
Azure SQL Database provides several built-in masking functions, including partial masking, full masking, and random masking.
Yes, you can create your own custom masking function in Azure SQL Database.
To define the columns to be masked, you need to determine which columns in your database contain sensitive data that needs to be masked.
After selecting the masking function, you need to create a masking policy that specifies which columns to mask and which masking function to use. You can create the masking policy using T-SQL commands or the Azure portal.
Before implementing the masking policy, it is important to test it to ensure that it is working as expected. You can do this by running sample queries on the masked data to confirm that the sensitive data is being properly masked.
The purpose of dynamic data masking in Azure SQL Database is to provide an additional layer of protection that can help prevent unauthorized access to sensitive data.
No, dynamic data masking is not a replacement for other security measures, such as encryption or access control. It is an additional layer of protection that should be used in conjunction with other security measures.
No, dynamic data masking is supported in certain versions of Azure SQL Database. You should check the documentation to see which versions of Azure SQL Database support dynamic data masking.
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