To test an application, you prefer to use data that matches reality as closely as possible. But many software applications use personal data. To properly test (new features of) an application, you'd rather not use this data. This is why we have developed the Enshore Data Mask. Testing with masked data for the most realistic results, without the risk of leaking sensitive data.
Applicable to any database
Masking a table with a billion records means that the tool can handle 99.9% of all tables by size. Thus, the data masking tool is applicable to (almost) any database. Whereas for this application we chose to completely anonymize the data, we also made it possible to pseudonymize data. In both cases the test data is still usable, but with the use of pseudonyms the test data can still be traced back to the original data. We use this feature for Logspect. Our tool that allows organizations to better ensure the privacy of their customer and patient data.
Masking a billion records
The Enshore Datamasker was developed for an organization that works with large data sets on a daily basis. The challenge was to mask a dataset with a billion records. Only the columns containing personal data needed to be masked. Because the data does not need to be traced back to the original data, we chose to anonymize it completely.
Anonymize
When anonymizing personal data, it is impossible to restore the masked data to the original data. This is necessary in situations where testers and developers do not have permission to view personal data. The new AVG states that in these situations, organizations are only compliant if they use anonymized testing environments.
Pseudonymizing
Sometimes it is desirable to be able to trace data after they have been masked. This pseudonymization is then done by replacing personal data with so-called synonyms. These synonyms can then be converted back to real personal data.
Use data mask?
We offer the data mask through our partner Nestor Security. Working together, we implement the tool and ensure that you can test without using sensitive data. Optimal results with the minimum risks.