AI regulation differs by country and sector. Teams should not wait for one global rule or rely on a generic compliance claim from a provider.
Know the system and use
Keep an inventory with purpose, affected people, data, owner, model, provider, product, API route and status. Record the countries and sectors involved.
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Classify impact
Identify possible harm to rights, safety, work and access to services. High-impact uses need stronger evaluation, documentation, oversight and routes to challenge outcomes.
Keep evidence during the lifecycle
Retain the business case, data basis, test set, results, approvals, incidents and changes. Version important prompts, models and workflows. Evidence created during the work is easier to trust than documents written after an incident.
Add local legal review
Map applicable data, consumer, employment, sector and AI rules in every market. Use current official sources and qualified advice. State when a framework is voluntary, proposed or legally binding.
Monitor change
Assign an owner to watch regulators, provider terms and model retirements. Review the system when its law, use, data or technology changes.
Use one control more than once
A clear inventory, evaluation set and incident process can support several legal and governance duties. Map each requirement to the evidence instead of creating separate documents that drift apart. Keep a named owner for the mapping.
Your next step
Choose one AI service and assemble its minimum regulatory evidence pack, including the exact local requirements and next review date.