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Introduction to Dynamic Data Masking

What is it

SQL Server Dynamic Data Masking is a new feature which has been released with SQL Server 2016. It is designed to allow the creation of a pre-defined rule which can be applied to the data in a column limiting the exposure of the actual data.

For example, if you have the following password column in a table which contains user passwords and has a datatype of nvarchar;

Password

Kingston123

Sh@r0na

you can apply a data masking rule to the column which will make is appear as the following to any unauthorised user;

Password

XXXX

XXXX

Masking Options

Dynamic Data Masking currently has 4 possible masking options which can be applied to a column;

Default

This will mask the full value depending on the column’s data type.

For string data types; char, nchar, varchar, nvarchar, text and ntext the value will be replaced with XXXX. If the length of the field is less than 4 characters, then that number of X’s will be used.

For numeric data types; bigint, bit, decimal, int, money, numeric, smallint, smallmoney, tinyint, float and real the value will be replaced with 0.

For date and time data types; date, datetime2, datetime, datetimeoffset, smalldatetime and time the value will be replaced with 01.01.2000 00:00:00.0000000.

For binary date types; binary, varbinary and image the value will be replaced with 0.

Email

This will mask the full value exposing the first letter of the string and masks the rest with XXX@XXXX.com. For example, ‘james.russell@myemail.co.uk’ will be masked as ‘jXXX@XXXX.com’.

Custom String

This will mask part of a value exposing a number of characters at the start of end of the string based on a prefix and suffix padding value. For example, ‘abcdefghij’ when given a prefix padding of 2 and a suffix padding of 4 will be masked as ‘abXXXXghij’.

Random

This can be used with any numeric data type and will mask the original value with a random value based on the supplied range. For example, ‘123456789’ when given a range of 1-5 will be masked as either 1, 2, 3, 4 or 5.

Uses and Limitations

Dynamic data masking is designed to restrict the exposure of sensitive to non-privileged users with minimal impact on the application layer. As its application only effects the result set of a query over designated database fields while keeping the actual data in the database the same, it is perfect for reporting or Business Intelligence uses; this also means that it can be incorporated without modifying pre-existing queries.

This feature is implemented by running Transact-SQL commands in SQL 2016 or by using the Azure portal for Azure SQL Databases.

It is important to note that dynamic data masking is not designed with the purpose of extensive database security and will not be able to prevent database users from running intrusive queries to expose extra pieces of the sensitive data by connecting directly to the database. The feature can however be used in conjunction with other SQL security features such as encryption, row level security and auditing.

There are a number of other restrictions\applications that should be noted;

· Dynamic data masking will not work with; encrypted columns using Always Encrypted, FILESTREAM column types, COLUMN_SET or a sparse column that is part of a column set, computed columns (if the computed column depends on a masked column then the result will be masked data), keys for a FULLTEXT index.

· Updates can still be made to a masked column, even though when queried the user will see masked data.

· Using SELECT INTO or INSERT INTO to copy data from a masked column into another table will result in masked data in the target table.

· When using SQL Server import and export functionality on a database containing masked data the resulting backup file or imported table will contain masked data.

I will be posting a future blog showing in-depth real-life applications of dynamic data masking with examples, applied permissions and further applications for both on-premises SQL 2016 and Azure SQL Databases.

Further Reading

I will be posting a future blog showing in-depth real-life applications of dynamic data masking with examples, applied permissions and further applications for both on-premises SQL 2016 and Azure SQL Databases.

For more detailed information see the following links;

       MSDN Dynamic Data Masking, Get started with SQL Database Dynamic Data Masking (Azure Portal)

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