Data masking pertains to the concealing of the original data with modified or false content. The objective is to protect the original data, especially if it is classified or highly sensitive, while making sure that it is still useful for valid test cycles. PostgreSQL dynamic data masking is among the applications of this process. High-quality web-based database client tools do that according to a predefined policy. Using central policy management, administrators can allow access only to the most necessary data to end users. This way, production data access is still possible, while helping administrators save a lot of time and money by eliminating the need to build a separate database.
Effective PostgreSQL dynamic data masking also enables customers to attain GDPR data protection. With dynamic masking, the concealing of data is done upon request, and the data does not have to leave your database. The masking tools proxies inbound and outbound queries. If someone wants to see the sensitive data in one of the masking rules, the database client will provide the masked data to them.
Converting actual data values into masked values is necessary to prevent deliberate breaching and accidental leakage of data. This makes PostgreSQL dynamic data masking an important process when working with third-party companies, such as testers and developers. Companies with sales teams working with clients and in need of constant access to the database can also benefit from data masking.
Static masking is another type of data masking process. Just like PostgreSQL dynamic data masking, it creates proxies with temporary masking rules, and to extract data, a request must be made. However, it could put your database at risk of being compromised during extraction, and it will require you to actively keep it up-to-date. Although static masking is recommended when sharing your data with third-party entities, you may consider dynamic data masking as a more secure alternative for any application.