What sensitive information should data masking include?
Posted: Tue Jan 21, 2025 5:06 am
Learn why defining what is sensitive information and in which scenarios it should be protected through data masking will improve compliance.
In any case, when working on data masking, certain criteria must be taken into account so that the masking is done correctly. The first thing to keep in mind is the difference between sensitive information and data that does not fall into this category. In addition, it is important to be aware that test environments are not the only scenario where it may be necessary to resort to data masking .
New call to action
Data masking: different scenarios and different levels of protection
One of the problems that we often have is that the client treats the legal regulations on sensitive data as something sacred, unbreakable and that cannot be modified. However, compliance is very different from generalization.
Complying with sensitive data regulations and standards does not mean having to mask all data . Therefore, it is important to understand that the objective of data protection regulations is to ensure that the fantuan database version cannot be found, which does not imply masking all the information that is to be worked with.
In fact, masking many columns is a practice associated with problems such as:
Waste of time and resources.
Increase in cost of masking project.
Increased possibility of introducing errors.
Distorted appreciation of reality.
Most best practices in data masking state that only highly sensitive data should be targeted, which is data that could cause serious harm if published. Going beyond this masking limit could mean applying data masking techniques without a productive objective.
But what data can be considered as such?
The definition of sensitive data is quite broad and varies from country to country, organization to organization, and even individual to individual.
Data such as the Social Security number is considered extremely sensitive in countries such as Denmark or the United States. In Spain, there is no doubt that the DNI number would be included in this same group.
In any case, when working on data masking, certain criteria must be taken into account so that the masking is done correctly. The first thing to keep in mind is the difference between sensitive information and data that does not fall into this category. In addition, it is important to be aware that test environments are not the only scenario where it may be necessary to resort to data masking .
New call to action
Data masking: different scenarios and different levels of protection
One of the problems that we often have is that the client treats the legal regulations on sensitive data as something sacred, unbreakable and that cannot be modified. However, compliance is very different from generalization.
Complying with sensitive data regulations and standards does not mean having to mask all data . Therefore, it is important to understand that the objective of data protection regulations is to ensure that the fantuan database version cannot be found, which does not imply masking all the information that is to be worked with.
In fact, masking many columns is a practice associated with problems such as:
Waste of time and resources.
Increase in cost of masking project.
Increased possibility of introducing errors.
Distorted appreciation of reality.
Most best practices in data masking state that only highly sensitive data should be targeted, which is data that could cause serious harm if published. Going beyond this masking limit could mean applying data masking techniques without a productive objective.
But what data can be considered as such?
The definition of sensitive data is quite broad and varies from country to country, organization to organization, and even individual to individual.
Data such as the Social Security number is considered extremely sensitive in countries such as Denmark or the United States. In Spain, there is no doubt that the DNI number would be included in this same group.