The process to santize data with the intent to get compliant to data protection regulations
Many organizations use dozens of databases and applications for their business processes. With the updated and/or newly introduced data privacy regulations, data anonymization is getting more important every day. In software development and quality it is quite common to copy databases for these processes. But a lot of these db’s contain privacy sensitive personal data or corporate critic data. How do you deal with this? In this solutions article we inform you about test data anonymization in a broad sense.
What is data anonymization?
Anonymizing data is the process of changing Personally Identifiable Information (PII) in such a way that it is not traceable to a natural living person anymore. You may not be able to identify the original person behind the data. This can be achieved by masking the data or generating synthetic data.
What does data anonymization mean?
Before we go into what anonymization means, we need to know when data is privacy sensitive. A name for example is personal, but not privacy sensitive. The city that you live in isn’t either. It is public information, information that you can find out by just googling someone’s name. But the fact that you have a huge debt or a disease makes your data privacy sensitive. In this example, by separating name, city, disease and debt, the data cannot refer back to a certain living person and therefor it is not privacy sensitive anymore. By separating this data, by masking it or by generating synthetic data, it is anonymized.
Why do we anonymize data?
No one wants their personal data to end up on the street. That’s why most governments have data privacy laws like GDPR, PCI and HIPAA to protect customers – civilians – from wrongdoing. One should not be using privacy sensitive data for other purposes than the initial permission. Not securing the data properly, every organization risks the following:
- Not complying with data privacy laws and European Union directive concerning data protection
- Exposure of privacy sensitive data to unauthorized users
- Image loss because of bad publicity when data is leaked
- Customers that terminate their relation due to lack of trust in security
In order to prevent risking the above, you need to make sure your test data is anonymized which can be done with several tools and techniques.
Meet the GDPR by anonymizing data
If you anonymize your data (remove any sensitive values or records) in non-production environments you comply with the GDPR. There is no need for compliance when you only have anonymized data, as shown in recital no 26 of the GDPR. This is especially interesting from a data security perspective: data anonymization enables you to at least lower your guard for protecting data in these settings because there is less risk.
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