Navigating the Global AI Regulatory Maze: Security and Policy Perspectives

(Note: The second part of the Arm AI Readiness Index report is live today, April 3, along with part 1. This blog is excerpted from the chapter “AI Policy, Regulation and Global Trends.”)

The emergence of artificial intelligence has generated unprecedented excitement about its potential to transform industries, but this enthusiasm is tempered by caution. Businesses are approaching AI deployment with deliberation, concerned about risks ranging from misuse to unintended consequences. This cautious approach mirrors historical patterns where transformative technologies prompted government intervention to establish “rules of the road” – as we’ve seen with automobiles, pharmaceuticals, and chemical production.

The regulatory landscape for AI remains fragmented globally, largely due to a fundamental lack of technical understanding and consensus on potential harms. Without agreed-upon frameworks, businesses hesitate to fully embrace AI capabilities, while governments struggle to keep pace with the technology’s rapid evolution. Even defining what constitutes AI risk varies dramatically across jurisdictions.

While most AI applications present minimal risk, certain use cases demand closer scrutiny. Governments are increasingly focusing on these higher-risk scenarios, particularly where AI systems operate as “black boxes” with limited transparency. This opacity can lead to troubling outcomes – from biased hiring practices to unfair denial of public benefits – with no clear mechanism to identify or remedy the root causes.

The Global Regulatory Divide

The global divide on AI regulation reflects different national priorities and cultural perspectives. Countries with established AI safety institutes, such as the UK, Japan, and South Korea, show stronger alignment on risk assessment. Meanwhile, the Organization for Economic Cooperation and Development (OECD) nations maintain more common ground than the broader United Nations General Assembly, where perspectives from Saudi Arabia, China, Malaysia, Iran, Rwanda, the United States, and Brazil diverge significantly.

Contrasting Approaches: U.S. vs. EU

The U.S. has historically favored a sector-specific approach to technology regulation, and AI appears to be following this pattern. Unlike the EU’s comprehensive AI Act, which imposes broad cross-sectoral restrictions, U.S. regulations will likely target specific contexts:

• Personal harm prevention (banning non-consensual deepfakes)
• Financial guardrails (parameters for AI in banking and trading)
• Healthcare guidelines (standards for AI in medical diagnostics)

This tailored approach provides flexibility within industries without imposing overarching restrictions that could hamper innovation.

With limited federal action, states are filling the regulatory void. California’s Senate Bill 1047 and Colorado’s AI law (effective February 2026) signal a growing trend of state-level initiatives, creating a patchwork of regulations that businesses must navigate.

The European Framework

The EU approaches AI with heightened concern about existential risks, reflected in its comprehensive AI Act. While this legislation includes extraterritorial provisions similar to GDPR, its international impact remains uncertain. Some companies have already chosen to avoid certain markets rather than navigate complex compliance requirements.

However, there are signs of regulatory recalibration. European Commission leadership has indicated a desire to reevaluate previous regulatory actions, with President Ursula von der Leyen suggesting a pause to assess whether existing policies have achieved their intended effects or merely restricted European competitiveness.

Sandboxes vs. Innovation

Regulatory approaches also differ in implementation. The EU and UK favor “sandbox” environments where developers can test technologies under protective frameworks before facing broader compliance requirements. The U.S. is unlikely to adopt this model, instead focusing on enforcement through audits and public disclosures.

A balanced approach would encourage innovation while addressing risks, allowing products to reach the market with ongoing monitoring and safeguards against misuse.

As AI moves from purely digital domains to physical embodiment in robots, new regulatory considerations emerge. Japan’s use of robots in hospitality settings exemplifies this shift, raising questions about liability, security, and integration into existing legal frameworks.

Recent regulations like the EU Product Liability Act shift accountability upstream in the supply chain, including technology providers alongside manufacturers. This evolution suggests that as AI increasingly drives physical systems, regulatory scrutiny will intensify.

AI as Critical Infrastructure: Regulatory Implications

While early AI regulation focused primarily on software and model development, attention is shifting to the critical role of computing power. Governments worldwide now recognize that software capabilities depend entirely on hardware infrastructure.

This recognition has prompted significant investments in computational capacity across the U.S., Europe, Southeast Asia, and beyond. Just as electricity became a ubiquitous utility, AI is positioned to become the next foundational layer of global infrastructure, requiring robust compute resources.

This evolution toward AI as a critical utility introduces new regulatory considerations. As with other essential infrastructure like electricity or telecommunications, governments must balance ensuring widespread access with appropriate oversight. This suggests a potential regulatory shift from purely focusing on AI applications toward frameworks that address the underlying compute infrastructure. Questions emerge about who controls this infrastructure, how it’s distributed, and ensuring equitable access while maintaining security standards. Just as electricity grids are subject to reliability and safety regulations rather than controlling what appliances consumers use, we may see a similar multi-layered approach for AI – with baseline regulations for the computational foundation and more targeted guidelines for specific high-risk applications built upon it.

Finding Balance

The path to balanced AI regulation remains challenging but promising. Governments increasingly acknowledge AI’s transformative potential and the infrastructure needed to support it. As with early automobiles, where safety features like seatbelts and airbags weren’t immediately mandated, AI regulation will likely evolve through iterative improvement.

The ultimate goal is clear: developing regulatory frameworks that mitigate harm while enabling innovation. This balanced approach requires ongoing collaboration between governments, industry stakeholders, and the public to ensure AI serves as a tool for progress rather than a source of harm.

As organizations navigate this complex landscape, staying informed about evolving regulations across regions becomes essential. By understanding these diverse approaches, businesses can develop strategies that comply with regional requirements while maximizing AI’s benefits. Through this careful navigation, we can collectively shape a future where AI’s transformative potential is realized responsibly and ethically.

Next week (April 10) we’ll release the Arm AI Readiness Index report in full, with part three, focusing on sustainable AI implementation, building an AI-ready culture, addressing workforce skills challenges.

To access Parts 1 and 2 of the report and begin your AI readiness assessment, visit this Arm.com page.