Dynamic data masking helps prevent unauthorized access to sensitive data by enabling customers to designate how much of the sensitive data to reveal with minimal impact on the application layer. DDM can be configured on the database to hide sensitive data as the result sets of queries over designated database fields, while the data in the database is not changed.
SQL Server 2016 introduced three new principal security features – Always encrypted, Dynamic Data Masking and Row Level Security. In my earlier blog I had written about “Always encrypted”. In this blog I am going to introduce Dynamic Data Masking (DDM) feature in SQL Server 2016.
Dynamic Data Masking Feature:
Dynamic data masking ﴾DDM﴿ limits sensitive data exposure by masking it to non‐privileged users. Dynamic data masking helps prevent unauthorized access to sensitive data by enabling customers to designate how much of the sensitive data to reveal with minimal impact on the application layer. DDM can be configured on the database to hide sensitive data as the result sets of queries over designated database fields, while the data in the database is not changed.
Dynamic data masking is easy to use with existing applications, since masking rules are applied in the query results.
Dynamic data masking is available in SQL Server 2016 and Azure SQL Database, and is configured by using Transact‐SQL commands. For additional information about configuring dynamic data masking by using the Azure portal, see https://docs.microsoft.com/en-us/azure/sql-database/sql-database-dynamic-data-masking-get-started which is not covered in this article.
How DDM works?
Dynamic Data Masking rules can be defined on particular columns, indicating how the data in those columns will appear when queried. There are no physical changes to the data in the database itself; the data remains intact and is fully available to authorized users or applications. Database operations remain unaffected, and masked data has the same data type as the original data, so DDM can often be applied without making any changes to database procedures or application code.
To add a data mask on a certain column in the database, all you need to do is to alter that column by adding a mask and specifying the required masking type. Four types of masks are available; you can choose default masking, which fully masks out the original value, partial masking where you can specify part of the data to expose, or random masking, which replaces the numeric value with a random value within a specified range. There is also an email masking function, which exposes the first character and keeps the email format.
Full masking according to the data types of the
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 01.01.2000
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: Phone#
varchar(12) MASKED WITH (FUNCTION =
Example alter syntax: ALTER COLUMN Gender
ADD MASKED WITH (FUNCTION =
Masking method which exposes the first and last
letters and adds a custom padding string in the
Example definition syntax: FirstName
varchar(100) MASKED WITH (FUNCTION =
Example alter syntax: ALTER COLUMN [Phone
Number] ADD MASKED WITH (FUNCTION =
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: Account_Number
bigint MASKED WITH (FUNCTION =
'random([start range], [end range])')
Example alter syntax: ALTER COLUMN [Month]
ADD MASKED WITH (FUNCTION = 'random(1,
Masking method which exposes the first letter of an
email address and the constant suffix ".com", in the
form of an email address. . aXXX@XXXX.com.
Example definition syntax: Email
varchar(100) MASKED WITH (FUNCTION =
Example alter syntax: ALTER COLUMN Email
ADD MASKED WITH (FUNCTION = 'email()'﴿
You can also configure masking functions on columns at the time of table creation:
Creating a table with Dynamic Data Masking:
CREATE TABLE [CUSTOMER] (
[CustId] INT IDENTITY (1,1) NOT NULL,
[Title] NVARCHAR(8) NULL,
[FirstName] VARCHAR(32) NOT NULL,
[LastName] VARCHAR(32) MASKED WITH (FUNCTION='partial(2,"----",0)') NOT NULL,
[EmailAddress] NVARCHAR(50) MASKED WITH (FUNCTION='email()') NOT NULL,
[Phone] NVARCHAR(128) MASKED WITH (FUNCTION='default()') NULL,
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. 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.
Best Practices and Common Use Cases
Creating a mask on a column does not prevent updates to that column. 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.
Using SELECT INTO or INSERT INTO to 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 a backup 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.
- 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 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 LastName column:
ALTER COLUMN LastName ADD MASKED WITH (FUNCTION = 'partial(2,"XXX",0)');
Granting Permissions to View Unmasked Data
Granting the UNMASK permission allows TestUser to see the data unmasked.
GRANT UNMASK TO TestUser;
Removing the UNMASK permission to Testuser
‐‐ Removing the UNMASK permission
REVOKE UNMASK TO TestUser;
Dropping a Dynamic Data Mask
The following statement drops the mask on the LastName column created in the previous example:
ALTER TABLE TableName
ALTER COLUMN LastName DROP MASKED;
Conclusion: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 this feature in conjunction with them in addition in order to better protect the sensitive data in the database.