5 Strategies for Cross-Jurisdictional AI Risk Management
As of the end of 2024, more than 70 countries have published or are drafting regulations specific to artificial intelligence (AI). These regulations often include varying definitions of what constitutes responsible use, leading to a complex landscape for organizations operating globally.
The challenge lies in navigating this growing patchwork of laws. For instance, the US government’s approach emphasizes responsible AI adoption across the economy by focusing on compliance with existing laws rather than creating new ones. In contrast, the EU AI Act introduces rigorous, risk-based classifications that impose strict obligations on providers, deployers, and users of AI.
To effectively manage these risks, organizations need strategic approaches adaptable to different regulatory environments. Here are five key strategies for cross-jurisdictional AI risk management:
1. Map Your Regulatory Footprint
Understanding where AI tools are developed, as well as where their outputs and data flow, is essential. An AI model created in one jurisdiction may be deployed or retrained in another without awareness of new regulatory obligations. Organizations should maintain an AI inventory that details each use case, vendor relationship, and dataset, categorized by geography and business function. This process clarifies applicable laws and highlights potential risks, such as using US consumer data to inform decisions about European customers. Think of this inventory as a compliance map for AI that evolves alongside your technology and global footprint.
2. Understand the Divides That Matter Most
Compliance risks arise from assuming AI is regulated uniformly across regions. The EU AI Act classifies AI systems by risk level—minimal, limited, high, or unacceptable—and imposes detailed requirements for high-risk applications including hiring, lending, healthcare, and public services. Non-compliance can lead to fines up to €35 million or 7% of global annual revenue.
In contrast, the US lacks a cohesive federal framework, resulting in varying state-level regulations such as those in California, Colorado, and Illinois. Multinational organizations may therefore need multiple compliance models for a single product. For example, a generative AI assistant may be low-risk in the US but classified as “high-risk” under European rules.
3. Ditch the One-Size-Fits-All Policy
AI governance should establish universal principles—fairness, transparency, and accountability—but should not enforce identical controls across jurisdictions. Rigid frameworks can stifle innovation in some regions while failing to meet compliance in others. Instead, develop governance that scales based on intent and geography. Implement global ethical standards complemented by regional guidelines and rules to maintain consistency while allowing flexibility for EU documentation requirements and varying state laws.
4. Engage Legal and Risk Teams Early and Often
AI compliance is evolving rapidly, requiring legal teams to be involved from the outset of AI design and deployment. Cross-functional collaboration is crucial; technology, legal, and risk teams must share a common understanding of terminology related to AI use, data sources, and vendor dependencies. Misalignments can cause governance blind spots. Integrating legal perspectives into model development enables informed decisions about documentation and third-party exposure before regulatory inquiries arise.
5. Treat AI Governance as a Living System
AI regulation is not static. As the EU AI Act evolves and US states propose regulations, compliance will remain a moving target. Organizations must view governance not as a one-time initiative but as an evolving ecosystem. Continuous monitoring, testing, and adaptation should be integrated into daily operations rather than limited to annual reviews. Cross-functional teams should communicate intelligence among compliance, technology, and business units to ensure controls evolve alongside technological advances.
The bottom line is that while AI operates globally, its risks are localized. Each jurisdiction introduces unique variables that can compound if not managed properly. Viewing compliance as a static requirement treats risk as a one-time audit—both approaches fail to address the dynamic nature of this landscape.
Organizations that prepare for future challenges will see AI governance as an ongoing risk management process—identifying exposures early, mitigating them with clear controls, and building resilience into every stage of design and deployment.
