Security & GDPR
GDPR and data: a practical guide for leadership
What the GDPR requires when exploiting data, what responsibility falls on leadership and how to work with sensitive data without losing control or compliance.
Read articleKey takeaways
- Anonymisation removes the ability to identify a person; pseudonymisation reduces it.
- Masking hides sensitive data while keeping the format.
- They let you use data for analytics, AI and testing without exposing people.
- The right technique depends on the use case and the risk.
To exploit sensitive data without violating privacy, there are several techniques often confused with one another: anonymisation, pseudonymisation and masking. Understanding the differences helps leadership decide what to use in each case. The EDPB and national authorities provide guidance on where each is appropriate.
Anonymisation
It transforms data so that it is no longer possible to identify a person, even by combining it with other information. Once truly anonymised, data ceases to be personal data under the GDPR, which greatly widens its use.
Pseudonymisation
It replaces identifiers with pseudonyms, so a person cannot be identified without additional information kept separately. It reduces risk, but the data remains personal and under the GDPR (which explicitly recognises pseudonymisation in Art. 4).
Masking
It hides or replaces sensitive data (for example, an account number) while keeping the format — useful for development, testing or demos where the real value is not needed.
When to use each
- Broad analytics or sharing with third parties: anonymisation.
- Internal processes needing controlled re-identification: pseudonymisation.
- Testing and development: masking (or synthetic data).
The risk of re-identification
Anonymising is not simply deleting the name. By combining seemingly innocuous data (postcode, age, date) it is sometimes possible to re-identify a person. Serious anonymisation accounts for this risk and applies techniques that prevent it, because poorly anonymised data is still, legally, personal data.
Anonymisation vs. synthetic data
They are two complementary paths to use sensitive data without exposing people. Anonymisation transforms real data until it identifies no one; synthetic data generates new data with the same statistical properties. Depending on the case, one, the other or both make sense.
Anonymising and masking let you extract the value of data without exposing people.
Sources & further reading
Frequently asked questions
Are anonymisation and pseudonymisation the same?
No. Anonymisation makes identifying the person irreversible; pseudonymisation only makes it harder and the data remains personal.
Can I use anonymised data for AI?
Yes, it is one of its main uses: training and feeding models without processing personal data.
Which technique is best?
It depends on the use case and the acceptable risk level. Often several are combined within one project.
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