What is data masking?

Data Masking

To secure sensitive data, data masking is the technique of modifying some data components within data storage so that the structure remains similar, but the information is changed. Data masking ensures that sensitive client data is not accessible outside the allowed production environment. This is particularly frequent in settings such as user training and software testing.

Thanks to automated development and testing procedures, direct exposure to sensitive data is reduced. Nonetheless, data is essential in a variety of scenarios. Consider a bank that has outsourced some development work to overseas firms. Customer information is frequently illegal to leave the bank, regardless of the nation in which the bank is governed.

The offshored development firm can test the software using data similar to what would be observed in a live production environment by employing a data masking technique. Effective data masking needs data change to avoid re-engineering or identifying the original values. With encryption and decryption, data can be preserved, security policies can be demonstrated, and administration and security functions can begin to be separated. With encryption and decryption, data can be

Data Masking Types

  • Static Data Masking (SDM): Data is first masked in the database before being copied to a test environment, allowing companies to migrate test data to untrustworthy environments or third-party vendors.

  • DDM (Dynamic Data Masking): DDM eliminates the requirement for additional data storage. Data is left unmasked in the database until it is requested, at which point it is masked and transferred across. To conceal the data, the contents are shuffled in real-time on-demand. Unauthorized users are never given access to unmasked data. To achieve DDM, you'll need a reverse proxy. On-the-fly data masking is used to describe other dynamic data masking approaches.

What is the importance of data masking?

Data masking is necessary for many organizations for the following reasons −

  • Data masking addresses several significant dangers: data loss, data exfiltration, insider threats or account breach, and insecure third-party system interfaces.

  • Reduces the data security concerns that come with cloud adoption.

  • While keeping many of the data's intrinsic functional qualities renders it unusable to an attacker.

  • Authorized users, such as testers and developers, access data without exposing production data.

  • Data sanitization is possible because conventional file deletion leaves data traces on storage media, whereas sanitization masks the old values. Created with

Techniques for Masking Data

For data protection, you can employ a variety of masking techniques. Here are a few of the most popular −

  • Substitution is one of the most often used and successful data masking techniques. Real data is replaced with phony data that appear real when using this strategy. Phone numbers, zip codes, credit card numbers, Social Security and Medicare numbers, and other numbers are commonly substituted utilizing this manner. Real- life names can be randomly substituted from a given or modified search file when substituting names.

  • Another prominent method of masking data is shuffling. It's pretty similar to the substitution method described above, except that the substitution set is derived from the same column of data as the masked data. Said, the data is jumbled within the column at random.

  • Encryption is one of the most challenging data obfuscation techniques. To read data based on user rights and privileges, a specific encryption technique necessitates the usage of a "key."

  • Values can be nullified or deleted. Applying a null value to a field may appear to be a straightforward but practical approach to disguise data. On the other hand, this method is only beneficial for preventing direct data visibility. However, in most circumstances, this method of data masking will fail the logic of most programs; therefore, it is not as good or effective as it appears.

  • Variation in numbers and dates. When done correctly, number and date variance can provide you with relevant statistics without revealing sensitive financial information or transaction specifics. Assume you need to conceal the salaries of your staff.

  • When masked, you can apply the same variance to all salaries to ensure that the salary range between the top and lowest-paid employees remains accurate.

  • Character shuffle. It's a simple technique in which characters are jumbled into a random order to obscure the original information.

What Are Data Masking's Advantages?

The fundamental goal of security is to ensure data confidentiality to be comfortable that their information is safe. Masking done correctly can preserve data content while maintaining business value. There are several metrics for measuring masking degree, the most common of which is the K-Anonymity factor, but all of them should be subjected to shift left testing to verify data security and compliance.

Unlike encryption, which may be circumvented by devising techniques to gain user credentials, masking protects data in downstream environments in an irreversible manner.

Without programming expertise, consistent data masking while retaining referential integrity across heterogeneous data sources maintain sensitive data's security before being made available for development and testing or sent to an offsite data center or the public cloud.