The Law Didn't Change, But the Practical Standard Did
On March 31, 2026, Korea's Personal Information Protection Commission published a full revision of its Pseudonymized Data Processing Guideline. Because no statutory text changed, it is easy to miss — yet it directly affects how AI training data projects should be designed. Two changes matter most: the judgment standard has been standardized around a risk-based approach, and procedural and documentation burdens are now tiered by that risk level.
Previously, teams often faced a similar documentation load regardless of processing scale or sensitivity. The revised flow asks you to assess re-identification risk first, then scale the adequacy review and safeguards to match.
What a Risk-Based Regime Changes in Practice
The split into a main volume for newcomers and a supplementary volume for practitioners is telling. It assumes a division of labor: business units decide whether the data may be used at all, while data practitioners design which techniques apply and how far processing must go.
The Unstructured Data Problem
With text, images, and audio, identification risk shifts with context. The same sentence can pinpoint an individual once surrounding context is attached. Before feeding customer support logs, contracts, or field photos into LLM training or a RAG index, check the following.
The leftovers you thought you had deleted usually hide in these three places, not in structured columns.
Designing the Pipeline Around Compliance
Frequently Asked Questions
Do we need pseudonymization to fine-tune on internal data?
If the use stays within the purpose disclosed at collection, it may be possible without it. Once you move beyond that purpose, pseudonymization is the practical route.
What about prompts sent to an external LLM API?
Personal data inside a prompt raises third-party disclosure or processing-delegation questions. Verify the processing agreement, whether inputs are used for training, and the data region — then add a masking layer immediately before transmission.
Who is responsible when partner data is combined to build a model?
Each party remains responsible for its own data as a processor, but the contract must name who controls the combined output and spell out the prohibition on re-identification.
Readiness Checklist
How POLYGLOTSOFT Approaches This
We embed compliance requirements in code rather than in documents. Processing purpose and legal basis become required fields in the collection API schema, and masking and validation stages in the pipeline block risky attributes from ever reaching a training dataset. Because we work on a subscription model, when a guideline or notice is revised we reflect it in the very next sprint. If you are preparing an AI rollout and feel uncertain about your data processing basis, please get in touch.