Dynamic Data Masking
Applies to: SQL Server 2016 (13.x) and later Azure SQL Database Azure SQL Managed Instance Azure Synapse Analytics
Dynamic data masking (DDM) limits sensitive data exposure by masking it to non-privileged users. It can be used to greatly simplify the design and coding of security in your application.
Dynamic data masking helps prevent unauthorized access to sensitive data by enabling customers to specify how much sensitive data to reveal with minimal impact on the application layer. DDM can be configured on designated database fields to hide sensitive data in the result sets of queries. With DDM the data in the database is not changed. DDM is easy to use with existing applications, since masking rules are applied in the query results. Many applications can mask sensitive data without modifying existing queries.
- A central data masking policy acts directly on sensitive fields in the database.
- Designate privileged users or roles that do have access to the sensitive data.
- DDM features full masking and partial masking functions, and a random mask for numeric data.
- Simple Transact-SQL commands define and manage masks.
The purpose of dynamic data masking is to limit exposure of sensitive data, preventing users who should not have access to the data from viewing it. Dynamic data masking does not aim to prevent database users from connecting directly to the database and running exhaustive queries that expose pieces of the sensitive data. Dynamic data masking is complementary to other SQL Server security features (auditing, encryption, row level security...) and it is highly recommended to use it in conjunction with them in order to better protect the sensitive data in the database.
Dynamic data masking is available in SQL Server 2016 (13.x) and Azure SQL Database, and is configured by using Transact-SQL commands. For more information about configuring dynamic data masking by using the Azure portal, see Get started with SQL Database Dynamic Data Masking (Azure portal).
Defining a Dynamic Data Mask
A masking rule may be defined on a column in a table, in order to obfuscate the data in that column. Four types of masks are available.
|Default||Full masking according to the data types of the designated fields.
For string data types, use XXXX or fewer Xs if the size of the field is less than 4 characters (char, nchar, varchar, nvarchar, text, ntext).
For numeric data types use a zero value (bigint, bit, decimal, int, money, numeric, smallint, smallmoney, tinyint, float, real).
For date and time data types use 01.01.1900 00:00:00.0000000 (date, datetime2, datetime, datetimeoffset, smalldatetime, time).
For binary data types use a single byte of ASCII value 0 (binary, varbinary, image).
|Example column definition syntax:
Example of alter syntax:
|Masking method that exposes the first letter of an email address and the constant suffix ".com", in the form of an email address.
||Example definition syntax:
Example of alter syntax:
|Random||A random masking function for use on any numeric type to mask the original value with a random value within a specified range.||Example definition syntax:
Example of alter syntax:
|Custom String||Masking method that exposes the first and last letters and adds a custom padding string in the middle.
Note: If the original value is too short to complete the entire mask, part of the prefix or suffix will not be exposed.
|Example definition syntax:
Example of alter syntax:
You do not need any special permission to create a table with a dynamic data mask, only the standard CREATE TABLE and ALTER on schema permissions.
Adding, replacing, or removing the mask of a column, requires the ALTER ANY MASK permission and ALTER permission on the table. It is appropriate to grant ALTER ANY MASK to a security officer.
Users with SELECT permission on a table can view the table data. Columns that are defined as masked, will display the masked data. Grant the UNMASK permission to a user to enable them to retrieve unmasked data from the columns for which masking is defined.
The CONTROL permission on the database includes both the ALTER ANY MASK and UNMASK permission.
The UNMASK permission does not influence metadata visibility: granting UNMASK alone will not disclose any Metadata. UNMASK will always need to be accompanied by a SELECT permission to have any effect. Example: granting UNMASK on database scope and granting SELECT on an individual Table will have the result that the user can only see the metadata of the individual table from which he can select, not any others. Also see Metadata Visibility Configuration.
Best Practices and Common Use Cases
Creating a mask on a column does not prevent updates to that column. So although users receive masked data when querying the masked column, the same users can update the data if they have write permissions. A proper access control policy should still be used to limit update permissions.
INSERT INTOto copy data from a masked column into another table results in masked data in the target table.
Dynamic Data Masking is applied when running SQL Server Import and Export. A database containing masked columns will result in an exported data file with masked data (assuming it is exported by a user without UNMASK privileges), and the imported database will contain statically masked data.
Querying for Masked Columns
Use the sys.masked_columns view to query for table-columns that have a masking function applied to them. This view inherits from the sys.columns view. It returns all columns in the sys.columns view, plus the is_masked and masking_function columns, indicating if the column is masked, and if so, what masking function is defined. This view only shows the columns on which there is a masking function applied.
SELECT c.name, tbl.name as table_name, c.is_masked, c.masking_function FROM sys.masked_columns AS c JOIN sys.tables AS tbl ON c.[object_id] = tbl.[object_id] WHERE is_masked = 1;
Limitations and Restrictions
A masking rule cannot be defined for the following column types:
Encrypted columns (Always Encrypted)
COLUMN_SET or a sparse column that is part of a column set.
A mask cannot be configured on a computed column, but if the computed column depends on a column with a MASK, then the computed column will return masked data.
A column with data masking cannot be a key for a FULLTEXT index.
A column in a PolyBase external table.
For users without the UNMASK permission, the deprecated READTEXT, UPDATETEXT, and WRITETEXT statements do not function properly on a column configured for Dynamic Data Masking.
Adding a dynamic data mask is implemented as a schema change on the underlying table, and therefore cannot be performed on a column with dependencies. To work around this restriction, you can first remove the dependency, then add the dynamic data mask and then re-create the dependency. For example, if the dependency is due to an index dependent on that column, you can drop the index, then add the mask, and then re-create the dependent index.
Whenever you project an expression referencing a column for which a data masking function is defined, the expression will also be masked. Regardless of the function (default, email, random, custom string) used to mask the referenced column, the resulting expression will always be masked with the default function.
Cross database queries spanning two different Azure SQL databases or databases hosted on different SQL Server Instances and involve any kind of comparison or join operation on MASKED columns will not provide correct results because the results returned from the remote server is already in MASKED form and not suitable for any kind of comparison or join operation locally.
Security Note: Bypassing masking using inference or brute-force techniques
Dynamic Data Masking is designed to simplify application development by limiting data exposure in a set of pre-defined queries used by the application. While Dynamic Data Masking can also be useful to prevent accidental exposure of sensitive data when accessing a production database directly, it is important to note that unprivileged users with ad-hoc query permissions can apply techniques to gain access to the actual data. If there is a need to grant such ad-hoc access, Auditing should be used to monitor all database activity and mitigate this scenario.
As an example, consider a database principal that has sufficient privileges to run ad-hoc queries on the database, and tries to 'guess' the underlying data and ultimately infer the actual values. Assume that we have a mask defined on the
[Employee].[Salary] column, and this user connects directly to the database and starts guessing values, eventually inferring the
[Salary] value of a set of Employees:
SELECT ID, Name, Salary FROM Employees WHERE Salary > 99999 and Salary < 100001;
Id Name Salary 62543 Jane Doe 0 91245 John Smith 0
This demonstrates that Dynamic Data Masking should not be used as an isolated measure to fully secure sensitive data from users running ad-hoc queries on the database. It is appropriate for preventing accidental sensitive data exposure, but will not protect against malicious intent to infer the underlying data.
It is important to properly manage the permissions on the database, and to always follow the minimal required permissions principle. Also, remember to have Auditing enabled to track all activities taking place on the database.
Creating a Dynamic Data Mask
The following example creates a table with three different types of dynamic data masks. The example populates the table, and selects to show the result.
-- schema to contain user tables CREATE SCHEMA Data; GO -- table with masked columns CREATE TABLE Data.Membership( MemberID int IDENTITY(1,1) NOT NULL PRIMARY KEY CLUSTERED, FirstName varchar(100) MASKED WITH (FUNCTION = 'partial(1, "xxxxx", 1)') NULL, LastName varchar(100) NOT NULL, Phone varchar(12) MASKED WITH (FUNCTION = 'default()') NULL, Email varchar(100) MASKED WITH (FUNCTION = 'email()') NOT NULL, DiscountCode smallint MASKED WITH (FUNCTION = 'random(1, 100)') NULL ); -- inserting sample data INSERT INTO Data.Membership (FirstName, LastName, Phone, Email, DiscountCode) VALUES ('Roberto', 'Tamburello', '555.123.4567', 'RTamburello@contoso.com', 10), ('Janice', 'Galvin', '555.123.4568', 'JGalvin@contoso.com.co', 5), ('Shakti', 'Menon', '555.123.4570', 'SMenon@contoso.net', 50), ('Zheng', 'Mu', '555.123.4569', 'ZMu@contoso.net', 40);
A new user is created and granted the SELECT permission on the schema where the table resides. Queries executed as the
MaskingTestUser view masked data.
CREATE USER MaskingTestUser WITHOUT LOGIN; GRANT SELECT ON SCHEMA::Data TO MaskingTestUser; -- impersonate for testing: EXECUTE AS USER = 'MaskingTestUser'; SELECT * FROM Data.Membership; REVERT;
The result demonstrates the masks by changing the data from
1 Roberto Tamburello 555.123.4567 RTamburello@contoso.com 10
1 Rxxxxxo Tamburello xxxx RXXX@XXXX.com 91
where the number in DiscountCode is random for every query result.
Adding or Editing a Mask on an Existing Column
Use the ALTER TABLE statement to add a mask to an existing column in the table, or to edit the mask on that column.
The following example adds a masking function to the
ALTER TABLE Data.Membership ALTER COLUMN LastName ADD MASKED WITH (FUNCTION = 'partial(2,"xxxx",0)');
The following example changes a masking function on the
ALTER TABLE Data.Membership ALTER COLUMN LastName varchar(100) MASKED WITH (FUNCTION = 'default()');
Granting Permissions to View Unmasked Data
Granting the UNMASK permission allows
MaskingTestUser to see the data unmasked.
GRANT UNMASK TO MaskingTestUser; EXECUTE AS USER = 'MaskingTestUser'; SELECT * FROM Data.Membership; REVERT; -- Removing the UNMASK permission REVOKE UNMASK TO MaskingTestUser;
Dropping a Dynamic Data Mask
The following statement drops the mask on the
LastName column created in the previous example:
ALTER TABLE Data.Membership ALTER COLUMN LastName DROP MASKED;
Submit and view feedback for