The Governance Gap: Why AI Regulation Is Always Going to Lag Behind
Innovation evolves at machine speed, while governance moves at human speed. As AI adoption grows exponentially, regulation is lagging behind, which is typical with technological advances. Worldwide, governments and other entities are scrambling to regulate AI, but fragmented and uneven approaches abound.
The Nature of the Gap: Innovation First, Oversight Later
Regulatory lag is an inevitable byproduct of technological progress. For instance, Henry Ford wasn’t developing the Model T with a primary focus on highway safety and road rules. Regulatory patterns historically follow innovation; recent examples include data privacy, blockchain, and social media. AI’s rapid evolution outpaces policy formation and enforcement. In other words, the cart has been before the horse for a while.
Part of the challenge is that policymakers often react to harm rather than anticipate risk, which creates cycles of reactive governance. The issue isn’t the lag itself but rather the lack of adaptive mechanisms to keep up with emerging threat models, and the lack of will to compromise a competitive edge for the sake of safety. It’s a “race to the bottom” scenario; we’re eroding our own collective safety for localized competitive gains.
Global Patchwork of AI Governance Represents Fragmented Philosophies
The existing major AI governance approaches in the world vary greatly. In the EU, the AI Act introduced last year is very much ethics- and risk-based. AI use is assessed according to risk level, with some deemed unacceptable and therefore prohibited. The U.S., by contrast, has taken more of a regulatory sandbox model that emphasizes innovation flexibility. Some might describe it as a carve-out for innovation, while critics may call it a blank check.
There’s also the Hiroshima process, which contains global coordination intent but limited follow-through; each G7 nation is still focused on domestic AI dominance. In the U.S., the matter has largely been left up to states, ensuring a lack of effective regulation. States are creating new sandboxes to lure tech companies and investment, but meaningful regulation at the state level is unlikely; only exceptions are granted.
The UK has been in a domestic and international struggle to establish itself as fiercely independent following Brexit. Through deregulation and the government’s “Leveling Up” scheme, the introduction of regulatory sandboxes is no surprise. The UK government aspires for the UK to be a dominant AI superpower for both internal and external political advantage and stability.
The EU focuses more on consumer safety but also on the strength of its shared market. This makes sense, given the EU’s history with patchwork regulation. Shared compliance, norms, and cross-border commerce are key to making the EU what it is. They still require regulatory sandboxes, but each member state must have one operational by a set date.
The key point is that there are disjointed frameworks that lack shared definitions, enforcement mechanisms, and cross-border interoperability. This leaves gaps for attackers to exploit.
The Political Nature of Protocols
No AI regulation can ever be truly neutral; every design choice, guardrail, and regulation reflects underlying government or corporate interests. AI regulation has become a geopolitical tool; nations use it to secure economic or strategic advantage. Chip export controls are a current example; they serve as indirect AI governance.
The only regulation effectively introduced so far has been to intentionally hinder a market. The global race for AI supremacy keeps governance a mechanism for competition rather than collaborative safety.
Security Without Borders, but Governance With Them
The major thorny problem here is that AI-enabled threats transcend borders while regulation remains confined. Today’s rapidly evolving threats include both attacks on AI systems and attacks that use AI systems. These threats cross jurisdictions, but regulation remains siloed. Security is sequestered in one corner while threats cross the whole internet.
We’re already starting to see the abuse of legitimate AI tools by global threat actors exploiting weak safety controls. For example, malicious activity has been seen with the use of AI site creation tools that are more like site cloners and can be easily abused to spin up phishing infrastructure. These tools have been used to impersonate login pages for popular social media services and national police agencies.
Until governance frameworks reflect AI’s borderless structure, defenders will remain constrained by fragmented laws.
From Reactive Regulation to Proactive Defense
Regulatory lag is inevitable, but stagnation isn’t. We need adaptive, predictive governance with frameworks that evolve with the technology; it’s a matter of moving from reactive regulation to proactive defense. Ideally, this would look like:
- Development of shared international standards for AI risk classification.
- Broadened participation in standards-setting beyond major governments and corporations. Internet governance has sought (with mixed success) to use a multistakeholder model over a multilateral one. Though imperfect, it has made a huge impact on making the internet a tool for everyone and minimizing censorship and political shutdowns.
- Fostering diversity of thought in governance.
- A mechanism for incident reporting and transparency. A lack of regulations often means a lack of reporting requirements. It’s unlikely there will soon be a requirement to inform the public of damage from mistakes or design choices within regulatory sandboxes.
While the governance gap will never disappear, collaborative, transparent, and inclusive frameworks can prevent it from becoming a permanent vulnerability in global security.
