Data Sovereignty Emerges as the Starting Point for AI Adoption

According to AI TIMES, Everpure said on September 17 that the success or failure of AI adoption depends less on the model than on where data is stored and who controls it, stressing that data sovereignty must be reflected from the design stage, not after deployment. The presentation pointed out that 97% of organizations struggle to move AI projects into the operational stage, with dispersed and unprepared data cited as a major underlying cause. In environments where corporate data is scattered across multiple systems, departments, and countries, the same information can exist in different versions, and standards for use and protection can diverge. The report highlighted that in heavily regulated sectors such as finance, the public sector, and healthcare, companies must consider not only data location but also ownership, access rights, and jurisdiction. It also explained that having a data center in Korea or applying encryption does not end the sovereignty issue, and that contracts, legal structures, and geopolitical variables must be examined together. Ultimately, the message was that as AI spreads, companies need to design from the outset a system for finding and classifying the data they need, along with an architecture that is not locked into a specific platform and can be moved elsewhere.