Data masking is a technique of forming an architecturally similar but unoriginal edition of a company’s data that can be utilized for functions like user training and software testing. The purpose is to guard the original data while having a practical replacement for events when the actual data is not requisite.
Data masking is not identical to limiting the visibility of data in production databases from the users who do not have authorized access to it. In that case, the information is there in the database and is only not perceptible to the unauthorized users. There are a number of good and valid explanations for using this method in a production system, but implementing an approach that “data is there in the database but is hidden” to the security of information in test and development databases is a formula for the problem. The rationale is that stringent control measures are primed in production databases and these can present a cautiously managed outlook. Test and development systems are not similar. In general, they are a system in which usually there is much wider access. Information is evident to more users and those users frequently have low-level access and exclusive privileges. From the point of view of data visibility, a test or development system in which the information exists but concealed is a structure which will reveal its data sooner or later.
Data Masking helps in reducing risk of security breaches through compliance. By guarding sensitive information at the time of transferring production data with testers and developers, companies have been able to make sure that non-production databases have stayed in conformity with the policies of cyber security at the same time as allowing developers to perform production-class testing. It facilitates in increasing the efficiency through mechanization. By mechanizing the process of masking, companies have been able to decrease the workload on database administrators who formerly had to maintain masking scripts by hand.