Data masking

Data masking is the process of obscuring (masking) specific data elements within data stores. It ensures that sensitive data is replaced with realistic but not real data. The goal is that sensitive customer information is not available outside of the authorized environment. Data masking is typically done while provisioning non-production environments so that copies created to support test and development processes are not exposing sensitive information and thus avoiding risks of leaking. Masking algorithms are designed to be repeatable so referential integrity is maintained.

Common business applications require constant patch and upgrade cycles and require that 6-8 copies of the application and data be made for testing. While organizations typically have strict controls on production systems, data security in non-production instances is often left up to trusting the employee, with potentially disastrous results.

Creating test and development copies in an automated process reduces the exposure of sensitive data. Database layout often changes, it is useful to maintain a list of sensitive columns in a without rewriting application code. Data masking is an effective strategy in reducing the risk of data exposure from inside and outside of an organization and should be considered a best practice for curing non-production databases. It can be done in a copy THEN mask approach or a mask WHILE copy approach (the latter is branded as Dynamic Data Masking in some products).

Requirements

Effective data masking requires data to be altered in a way that the actual values cannot be determined or re-engineered, functional appearance is maintained, so effective testing is possible. Data can be encrypted and decrypted, relational integrity is maintained, security polices can be established and separation of duties between security and administration established. Common methods of data masking includes: encryption/decryption, shuffling, masking (i.e. numbers letters), substitution (i.e. All female names = Julie), nulling (####) or shuffling (zip code12345 = 53412).

Data Masking Techniques

Substitution

The Substitution technique replaces the existing data with random values from a pre-prepared dataset.

Shuffling

The Shuffling technique uses the existing data as its own substitution dataset and moves the values between rows in such a way that the no values are present in their original rows.

Number and Date Variance

The Number and Date Variance technique varies the existing values in a specified range in order to obfuscate them. For example, birth date values could be changed within a range of +/- 60 days.

Encryption

The Encryption technique algorythmically scrambles the data. This usually does not leave the data looking realistic and can sometimes make the data larger.

Nulling Out Or Deletion

The Nulling Out technique simply removes the sensitive data by deleting it.

Masking Out

If two tables contain the columns with the same denormalized data values and those columns are masked in one table then the second table will need to be updated with the changes. This technique is called Table-To-Table Synchronization.

References