The unpopular take is that restraint, not speed, may be the smarter strategy here.
Every time a major breach hits the headlines or a sophisticated attack exploits a newly discovered vulnerability, the regulatory impulse kicks into overdrive. We see calls for emergency legislation, mandatory reporting timelines that keep shrinking, and compliance frameworks that seem to multiply faster than the threats they're meant to contain. The latest wave of sophisticated attacks and supply chain compromises has naturally intensified this pressure.
But here's what keeps me up at night: we're about to legislate artificial intelligence security at a moment when we barely understand how AI itself works in production environments, let alone how to secure it properly.
The industry is moving fast, which is good. We're seeing real innovation in detection, response, and threat mitigation. Companies are learning from each other, sharing threat intelligence, and iterating on solutions in real time. This speed has value that shouldn't be underestimated. Yet the regulatory response threatens to freeze these practices into law before we've had a chance to see what actually works at scale.
Consider what happens when we mandate specific security measures before the technology matures. The EU's approach to data protection created real privacy gains, but it also created compliance theater. Organizations check boxes while security teams still struggle with the fundamentals. We risk building the same theater around AI security: compliance frameworks that satisfy regulators but don't actually make systems safer.
There's also the problem of regulatory capture. When government agencies move too quickly to regulate emerging technology, the first movers and largest players often shape the rules in their favor. Startups and smaller security vendors get locked out because they can't afford compliance infrastructure. The result? Less competition, slower innovation, and ultimately worse security outcomes for everyone.
I'm not arguing for no regulation. Bad actors need guardrails. But there's a meaningful difference between establishing principles and mandating implementation details. The regulatory impulse to specify exactly how companies must secure AI systems, how quickly they must disclose breaches, and what architecture decisions they must make is premature. We don't yet know which approaches will prove effective.
The cybersecurity field has made real progress when regulation focused on outcomes rather than methods. Breach notification laws set a clear expectation: tell people when their data is compromised. That simple principle has driven more security investment than any mandate about encryption algorithms or firewall configurations ever could. Why? Because companies still had freedom to innovate around the requirement.
AI security regulation needs the same approach. Define what responsible AI security looks like in terms of outcomes. Require companies to demonstrate that they're managing risks appropriately. But let them figure out how. Let the market reward effective approaches and punish ineffective ones. Let security teams experiment with new tools and methodologies without worrying they're violating some regulation written in 2025.
The alternative is what we're heading toward: legislation crafted by people who don't fully understand the technology, applied to companies racing to implement AI systems faster than regulatory guidance can keep up. That's a recipe for security theater dressed up as compliance.
Speed feels responsible when the threat environment is changing as fast as ours is. But wisdom sometimes means knowing when to pause. The attacks will keep coming. The vulnerabilities won't stop appearing. The bad actors will keep innovating. We'll have time to regulate if we get it right. But if we rush and build the wrong framework into law, we'll be stuck with it for years.
That's a risk we shouldn't be comfortable taking.