South Korea has rewritten the rules for working with pseudonymized data for AI: what should businesses take into their processes?
What’s important in the South Korean story is not that the regulator allegedly “allowed more data for AI.” This would be too simple and incorrect reading. In fact, the country is trying to do two things at the same time: leave a strict framework for personal data and at the same time remove unnecessary bureaucracy where business needs a clear, operational regime for AI development.
Therefore, news about Korean rules is useful not only for the local market. This is a good example of how privacy and AI governance can be collected not in two parallel folders, but in one managed system.
What exactly has changed
Starting point here - PIPC guidance on processing publicly available personal data for AI development and services, released in 2024. In it, the Korean regulator explained that publicly available data does not automatically turn into “nobody’s” data, but under certain conditions can be used for AI development based on legitimate interest.
The next step is to reassemble the rules for working with pseudonymized information. On official English page of PIPC on pseudonymization a basic rule is enshrined: pseudonymized data can be used without consent for statistics, scientific research and record preservation in the public interest, and the aggregation of such data between different controllers must occur through a designated specialized institution.
In the spring of 2026, South Korea went further and completely revised its operational guidelines to better suit AI-workflow. They also wrote about this Seoul Economic Daily, and PPC Land: the logic of the update boils down to reducing the paper load, more understandable risk assessment and adapting the rules to work with unstructured data sets and modern AI pipelines.
What doesn't it mean
The main mistake is to decide that now you can freely drag any data array into the model if you later “impersonalize” something. That's not how the Korean approach works.
First, publicly available data still requires an assessment of the legitimacy of the source, the purpose of use, and the risks to data subjects. Secondly, pseudonymization does not make the data anonymous. This is still personal data subject to a special processing regime. Third, data set merging and re-identification remain the most sensitive area of control.
This is why Korean logic is useful: it does not romanticize AI development, but forces you to describe the real steps of data processing.
What should a business take into its processes?
Even if you don't work in South Korea, there are three rules of thumb to take away from this case study.
First: separate publicly available data, pseudonymized data and anonymous data not in a management presentation, but in a processing registry. If inside the company all this is simply called “secure datasets”, you are already creating a problem for yourself.
Second, make decisions about data use at the scenario level, not at the abstract category level. The same set may be acceptable for internal analytics but too risky for model fine-tuning or an external AI service.
Third: tie privacy to the product life cycle. Korean documents are useful precisely because they look at data not in isolation from the product, but in stages: collection, filtering, pseudonymization, use, leak control, re-assessment of risks.
Minimum checklist for a team
Translating this into working language, the team should have answers to five questions:
- On what legal basis is each data source used?
- Which fields are really needed by the model or service, and which are left “just in case”?
- Where does pseudonymization end and the risk of re-identification begin?
- Who approves the merging of datasets and how is it documented?
- How do you ensure that the model does not reproduce sensitive or unnecessary personal information?
If there is no answer to at least two questions, then AI governance is still decorative.
Conclusion
South Korea does not rely on the slogan "AI at any cost." Her approach is interesting because it tries to remove unnecessary friction for the business, without blurring the area of responsibility. For product, legal, and compliance teams, this is a useful guideline: a good data practice is not one where there are fewer rules, but one where the rules are clear and integrated into the actual development process.