Global AI programs often fail on a practical detail: policies are not uniform across regions. Data residency rules differ. Allowed tools differ. Disclosure requirements differ. If these differences are handled ad-hoc in prompts, behaviour becomes inconsistent and audits become painful.
Use explicit policy packs
A useful pattern is to define policy packs per region and risk tier:
- Allowed data classifications and redaction rules.
- Approved providers and regions (see data residency).
- Tool availability and approval requirements (see tool authorisation).
- Disclosure and transparency expectations (see user transparency).
Then apply the pack as a runtime configuration, not just a prompt paragraph (see policy layering).
Make routing rules policy-aware
Routing should evaluate policy constraints explicitly: residency, safety tier, tool enablement, and cost ceilings. Record the evaluated rules and the chosen route (see routing and failover).
Log decisions for audits and incidents
Regional policy introduces audit questions: who approved a cross-border route, when did it happen, and which policy version applied? Decision logging with reason codes makes this traceable (see decision logging).
Test behaviour differences deliberately
Localisation should be tested like any other change:
- Regression suites per region for high-impact workflows (see prompt regression testing).
- Synthetic monitoring with golden queries per policy pack (see synthetic monitoring).
- Review loops for high-risk intents (see human review operations).
Keep the UX predictable
Users notice inconsistent refusals and tool availability quickly. If a region is in a more restrictive mode, communicate it clearly rather than failing silently. Clear release notes and transparency patterns prevent confusion (see release notes).
Policy localisation works when it is explicit, observable and tested. That makes regional differences manageable without turning every rollout into a bespoke governance project.