Tutorial / Cram Notes
What is Dynamic Data Masking?
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.
Benefits of Dynamic Data Masking
- Simplifies security management: No changes are needed on the application side.
- On-the-fly masking: Data in the database is not changed. Masking happens as query results are returned.
- Configurable: Different masking rules for different users.
Configuring Dynamic Data Masking
Step 1: Identify sensitive data
The first step is to identify the sensitive data that needs to be masked. Common examples include:
- Credit Card Numbers
- Social Security Numbers
- Email Addresses
- Phone Numbers
Step 2: Define masking rules
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:
- Default Full Masking: Non-fixed number of Xs (xxxx) for strings, 0 for numbers
- Custom String Masking: Define a custom mask with prefix and suffix
- Email Masking: Exposing only the first letter and constant suffix (xxxxx@example.com)
Step 3: Grant permissions
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 Permission: Users with this permission can see the unmasked data.
GRANT UNMASK TO [User/Role];
REVOKE UNMASK TO [User/Role];
- Standard database roles and DDM: Members of the
db_owner
anddb_datawriter
roles can see unmasked data by default.
Example: Implementing a Masking Rule
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 john.doe@example.com
, maintaining the first character and the domain structure.
Using Dynamic Data Masking with Azure Portal
To configure DDM via the Azure Portal, follow these steps:
- Navigate to your Azure SQL database in the Azure portal.
- Go to the “Security” section and click on “Dynamic Data Masking”.
- Click on “+ Add mask” to define a new masking rule.
- Specify the schema, table, and column that you want to mask, choose a masking function, and click “Add”.
- Save the changes.
Monitoring Dynamic Data Masking
- Query the
sys.masked_columns
view to see the masked columns. - Review the audit logs for attempts to access masked data.
Best Practices for Using Dynamic Data Masking
- Combine dynamic data masking with other security features like Azure Active Directory authentication, role-based access control, and advanced threat protection.
- Regularly review and update the masking rules as needed to adapt to any changes in the database schema or business requirements.
- Test the dynamic data masking implementation to ensure the right data is masked for the right users, and application functionality isn’t negatively impacted.
Conclusion
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.
Practice Test with Explanation
True or False: Dynamic Data Masking is supported on Azure SQL Database, Azure SQL Managed Instance, and SQL Server on Azure VMs.
- True
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.
True or False: Once a dynamic data mask is created, it cannot be altered or dropped.
- False
Correct Answer: False
Dynamic data masks can be altered or dropped after they have been created to adjust the mask or remove it entirely.
Which of the following is a type of dynamic data masking that can be used on SQL workloads? (Select all that apply)
- A) Default full masking
- B) Custom string masking
- C) Random masking
- D) Partial masking
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.
True or False: To configure dynamic data masking, you need to have control permission on the database.
- True
Correct Answer: True
Configuring dynamic data masking requires control permissions on the database to create, alter, or drop the masking rules.
Who among the following has the ability to view unmasked data in a database with dynamic data masking enabled by default?
- A) Database owner
- B) A user with SELECT permission
- C) A user with db_datareader role
- D) A user with the ‘UNMASK’ permission
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.
True or False: Dynamic data masking can be used as a primary security tool for protecting sensitive data at rest.
- False
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.
Which SQL statement is used to configure dynamic data masking on a column?
- A) ALTER TABLE
- B) CREATE MASK
- C) UPDATE MASK
- D) ALTER MASK
Correct Answer: A
The ‘ALTER TABLE’ SQL statement, along with appropriate dynamic data masking functions, is used to add a mask to a column.
What is the correct way to remove a dynamic data mask from a column in Azure SQL Database?
- A) DROP MASK FROM column_name
- B) ALTER TABLE table_name DROP MASK column_name
- C) ALTER TABLE table_name ALTER COLUMN column_name DROP MASK
- D) ALTER TABLE table_name ALTER COLUMN column_name DROP MASKED
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’.
True or False: When defining a dynamic data masking rule, you cannot use a default function to mask the entire field with the same character.
- False
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).
Which Azure role is required to manage dynamic data masking policies in Azure SQL Database?
- A) Contributor
- B) Reader
- C) SQL Security Manager
- D) Owner
Correct Answer: A
The Azure Contributor role has sufficient permissions to manage resources, including configuring dynamic data masking policies.
True or False: Dynamic data masking can be configured using the Azure portal, T-SQL commands, or PowerShell cmdlets.
- True
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.
In dynamic data masking, what does the function ’email()’ do?
- A) Masks the entire email address
- B) Reveals the entire email address
- C) Masks all but the first letter of the email username and the domain
- D) Masks the domain of the email address
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.
Interview Questions
What is dynamic data masking in Azure SQL Database?
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.
What are some sensitive data types that could be masked using dynamic data masking?
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.
What are some of the built-in masking functions provided by Azure SQL Database?
Azure SQL Database provides several built-in masking functions, including partial masking, full masking, and random masking.
Can you create your own custom masking function in Azure SQL Database?
Yes, you can create your own custom masking function in Azure SQL Database.
How do you define the columns to be masked 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.
How do you create a masking policy in Azure SQL Database?
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.
How do you test a masking policy in Azure SQL Database?
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.
What is the purpose of dynamic data masking in Azure SQL Database?
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.
Can dynamic data masking be used as a replacement for other security measures?
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.
Is dynamic data masking supported in all versions of Azure SQL Database?
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.
I followed the steps to configure dynamic data masking in SQL and it seems to work perfectly. Has anyone tested this under heavy workloads?
Is there any downside to using dynamic data masking in a live production environment?
I appreciate the detailed guide on configuring dynamic data masking. Keep up the good work!
Can dynamic data masking be combined with row-level security for finer control?
I tried the steps, but I’m facing issues when applying the masking rules to computed columns. Has anyone else encountered this?
The performance impact seems negligible for small to medium workloads, but has anyone scaled this to handle terabytes of data?
Thanks for the guide, it really helped me understand dynamic masking better!
For those using Azure SQL Database, do you enable dynamic data masking by default?