South Korea’s latest privacy reform could change which datasets businesses can use to develop artificial intelligence. Its commercial significance, however, lies in the conditions attached to that opportunity. Companies that can explain the necessity of identifiable data, demonstrate its provenance and substantiate safeguards may be better positioned than competitors that simply hold larger datasets.
On 20 August 2026, the National Assembly passed an amendment to the Personal Information Protection Act introducing special provisions for AI development. The Personal Information Protection Commission’s account describes a route for using lawfully collected personal information subject to enhanced safeguards and regulatory deliberation. It identifies circumstances involving material limitations on development using pseudonymised or anonymised information, or public-interest necessity. The announcement states that the amendment takes effect six months after promulgation; parliamentary passage is therefore not immediate permission to begin processing. PIPC’s announcement
A change in the investment case for data
Our assessment is that the reform could shift investment towards demonstrably usable data. A large collection of recordings or images has limited development value if a company cannot establish how it was collected, distinguish permitted uses or identify the people entitled to authorise access.
Consider a developer evaluating recordings for a fraud-detection model. It should be able to explain which characteristics of the recordings improve performance, what happens when identifying features are removed, and whether a narrower dataset achieves a comparable result. A general statement that more data produces better AI would provide little basis for assessing necessity.
That is a technical question with a legal consequence. Model teams would need to produce evidence that privacy teams can evaluate, while procurement teams would need assurances that the supplier’s collection practices can withstand scrutiny. Buying access to data and establishing a lawful basis to reuse it are separate decisions.
Build an application around a specific use
The PIPC’s description also identifies risk assessments and improvement plans for sensitive data or uniquely identifiable information, disclosure of the processing in privacy policies, and a streamlined approach for technologies or services similar to previously reviewed cases. These details suggest that implementation practice will be commercially important. PIPC’s explanation of safeguards and review
As a matter of project design, companies should prepare a bounded proposal: a defined model purpose, specified data categories, an access model and measurable safeguards. An approval request framed around an identifiable development task is easier to test than an open-ended claim covering all future training.
The evidence should extend beyond a security policy. Useful materials could include comparative model results using less identifiable data, access logs, tests for unintended disclosure and procedures for responding when a dataset is later challenged. These are recommended preparation measures, not a claim that every item is already a prescribed statutory filing requirement.
Commercial contracts should address what happens if permission is narrower than expected. A dataset purchased for several possible applications may support only one approved use. Milestone payments, restrictions on onward access and an agreed response to regulatory refusal can prevent that uncertainty from becoming a dispute between the developer and its supplier.
A wider reform remains under development
The AI amendment sits alongside a broader PIPC review. A consultation ran from 6 to 31 August 2026, with a reform plan intended by year-end. The regulator explicitly linked the exercise to unstructured data, organisational data sharing and agentic AI. Those proposals should be tracked separately from the amendment already passed. PIPC’s framework consultation
The distinction matters for deployment planning. A company should not treat the consultation’s direction as a legal basis, or assume that an AI-development provision necessarily authorises every subsequent production use, disclosure or international transfer. Each stage needs its own assessment.
The most useful signals over the coming months will be the promulgated text, subordinate rules, application requirements and the treatment of comparable cases. If review becomes predictable, the reform could reduce uncertainty for carefully governed projects. If the evidential burden or boundaries remain unclear, the immediate benefit may be concentrated among businesses with the resources to support detailed submissions. Either way, defensible data governance is becoming part of the economics of AI development.
Research updated 14 September 2026. Cover photograph: Yohan Cho.
