AI moves fast. Laws don't. Build governance that adapts across borders.
By the end of 2024, more than 70 countries had either passed or drafted AI-specific rules. What signals "responsible use" in one market can raise flags in another. If your models, data, or decisions cross borders, your governance needs to travel with them.
In the U.S., policy leans on existing laws and agency enforcement rather than a single federal framework. In the EU, the AI Act sets risk tiers and strict duties for providers, deployers, and users. A tool cleared for a U.S. sales team can trip high-risk obligations in Europe. The fix isn't more paperwork - it's smarter, adaptable governance built into how you design, deploy, and operate AI.
1) Map your regulatory footprint
You can't manage what you can't see. Create a living AI inventory that lists every use case, model, vendor, and dataset - tagged by geography, business unit, data type, user group, and decision impact.
- Track where training data originates, where inference happens, and where outputs are consumed.
- Flag cross-border flows (e.g., U.S.-trained models scoring EU customers).
- Record model purpose, inputs, outputs, human oversight, and explainability notes.
- Tie each entry to the laws and standards that apply in that location.
2) Understand the divides that matter most
The biggest risk is assuming every region treats AI the same. The EU AI Act uses risk classes and sets detailed obligations for high-risk areas like hiring, lending, healthcare, and public services. Fines can reach €35 million or 7% of global annual revenue. The U.S. relies on agency action and state rules focused on transparency, bias, and consumer protection.
- EU: risk-based duties and documentation; tighter transparency and monitoring requirements. See the EU AI Act overview.
- U.S.: enforcement via existing laws; active states include California, Colorado, and Illinois; agencies like the EEOC and FTC are policing discrimination and deceptive practices.
Bottom line: the same product may require different controls by region. Plan for that from day one.
3) Ditch the one-size-fits-all policy
Set global principles - fairness, transparency, accountability - but don't force identical controls everywhere. Build a layered model: universal standards at the top, regional implementation guides beneath, and use-case playbooks at the edge.
- Adopt a "high watermark" approach where practical: meet the strictest applicable rule to reduce rework later.
- Create regional addenda that map controls to local laws and define what "high-risk" means in that market.
- Document exceptions with clear business justification and compensating controls.
4) Bring legal and risk in early - and keep them there
Legal can't be a final checkpoint. Embed counsel and risk partners into discovery, model design, vendor selection, testing, and rollout. Use a shared intake form so everyone reviews the same facts: data sources, training methods, intended use, user groups, decision rights, and human-in-the-loop design.
- Create a common glossary for terms like "AI system," "training," "fine-tuning," "deployment," and "monitoring."
- Run pre-mortems: where could this system fail compliance in the EU vs. a U.S. state? Adjust controls before launch.
- Review vendor contracts for data use, retraining rights, subprocessor chains, and audit access.
5) Treat governance as a living system
Rules are moving. Your controls should be too. Make change detection and adaptation part of BAU, not an annual event.
- Stand up a regulatory watch function with a clear owner, update cadence, and a change log tied to control updates.
- Schedule periodic model testing for bias, performance drift, and documentation gaps - with evidence stored for audits.
- Define incident thresholds and response playbooks for data, model, and output issues across regions.
- Report governance KPIs to the executive team: inventory coverage, high-risk use cases approved, audits passed, issues resolved.
A 30-day plan for executives
- Week 1: Approve an enterprise-wide AI inventory template and mandate its use for new and existing projects.
- Week 2: Identify top 10 cross-border use cases and assign compliance owners for each.
- Week 3: Publish your global principles + regional addenda; define what "high-risk" means per market.
- Week 4: Launch a standing review with technology, legal, risk, and data leaders; set a monthly cadence.
The bottom line
AI is global. Risk is local. Treat compliance like a moving system, not a checkbox. The organizations that win build governance into the work: clear inventory, region-aware controls, early legal partnership, and continuous testing. That's how you scale AI with confidence across borders - without surprises.
Upskill your teams: If you're rolling out governance and need practical training by role, explore AI courses by job for fast, relevant enablement.
Your membership also unlocks:
