Each data masking platform substitutes the data factors with similar values, optionally moving masked data to a new location. Masking generates a proxy data replacement which maintains a fraction of the value of the original data. The main point is to create data that looks and acts like the original data, but which is less sensitive and doesn’t pose a risk of disclosure, allowing the use of tight security control measures for masked data repositories. As a result of it, the scope and difficulty of IT security efforts are reduced. Masking should work with basic data repositories, such as files and databases, without breaking it. The mask must make it impossible or unfeasible to overturn engineer masked values back to its original state without unique additional information, such as a shared secret or encryption key.
The following five rules help to capture the essence of Data Masking.
1. Masking should not be reversible:
Though you mask your organization’s sensitive data, it should be impossible to use it to regain the original data.
2. The outcome should be representative of the resource information:
The reason for masking data rather than just creating random information is to provide non-sensitive data that still bear a resemblance to production data for improvement and testing purpose. It may consist of geographic distributions or credit card distributions in which possibly the first four numbers are left unchanged, but the rest of the numbers are scrambled.
3. Must maintain the integrity of the referential data:
Masking solutions should not interrupt referential integrity of the data. For example, if a credit card number is a primary key, and jumbled as a component of masking, then all cases of that number connected through key pairs should be jumbled identically.
4. Mask the non-sensitive data only if it can be used to restore sensitive data:
It isn’t essential to mask all the information in your database, just mask those parts that you think are susceptible. But some non-sensitive data can be used to either reconstruct or tie back to sensitive data.
5. Masking should be a repeatable process:
One-off masking is not only almost impossible to retain, but it’s practically unsuccessful. Development and test information need to represent frequently changing production data as strictly as possible. Logical data may need to be created every day or even hourly. If masking isn’t an automatic process, it’s incompetent, costly, and ineffective.