Navigating AI Regulation Across Borders

Building one AI system that behaves identically in every country sounds efficient on paper, but it is not how regulation actually works. Data protection law in Ghana is not the same as data protection law in a neighbouring country, and a system designed with a single jurisdiction in mind tends to break, quietly or otherwise, the moment it operates somewhere else.

The starting point is therefore not choosing one regulatory framework to design around, but working out what stays consistent across different countries' rules and building on that rather than on the specific requirements of wherever the work happened to begin.

Data residency is usually where this gets tested first. Where identifiable health or financial data is permitted to live, and who is allowed to access it, varies by country, and it is not a question to resolve after a system is already storing data somewhere. We build on the assumption that identifiable data stays within the country it was collected in, which is stricter than some jurisdictions require, but it means the architecture does not need rebuilding each time we deploy somewhere with tighter rules than the last.

Interoperability standards follow the same logic. A health system that cannot exchange data in a recognised format such as FHIR R4 becomes an isolated island the moment it needs to connect to a national reporting system or to another facility's records. Building to a shared standard from the outset avoids that rebuild and lets the system connect to whatever infrastructure a country already has, rather than asking the country to adapt to us.

Access control is the third piece, and it has less to do with any single regulation than with a pattern that appears across nearly all of them: who can see what, and whether that access can be traced afterwards. A system with clearly defined role tiers and a tamper-evident record of every access holds up under most of the regulatory frameworks we have worked within, even where the surrounding paperwork looks very different from one country to the next.

Cross-border deployment does not become simple. What makes it manageable is separating the things that genuinely vary between jurisdictions, such as documentation formats and approval processes, from the things that hold steady across all of them, such as local data, shared standards and traceable access. Build on the second set and a system designed for one country can reach the next without being taken apart first.