Despite all the usual complaints about stifling innovation and overbearing compliance requirements, it seems like regulators might be tired of waiting for the AI industry to get its act together, and your test strategy needs to catch up.

In Test as Transformation – AI, Risk, and the Business of Reality, I talked about regulatory and oversight risk as one of the most underestimated problems in AI delivery: testing that looks legitimate, produces lots of reports, but doesn’t actually answer the questions regulators and auditors are going to ask.

Testing, when done right, should “bring insights into risk, anticipate issues, and surface patterns” in a way that stands up to scrutiny, not just make a dashboard go green.

Take what’s happening in South Korea right now, the government pushed through the AI Basic Act, viewed as one of the world’s first comprehensive AI laws. It requires labeling of AI-generated content, risk assessments for “high-impact” AI, and safety reporting for models with the teeth of fines and a formal enforcement regime.

This is exactly what I was hoping for when writing about “emergent regulation and yet to be revealed enforcement”, but I’ll try to management my expectations because we know from the Shawshank Redemption, “hope is a dangerous thing”.

Aligning your AI tests to frameworks like the EU AI Act and SS1/23 for financial services can’t just be the usual governance theater. A credible test approach can’t just say “model accuracy improved” or “hallucinations went down.” It has to show:

• how tests map to risk categories regulators care about
• how thresholds are set based on impact and criticality
• traceability and accountability for decisions on what/how/why you tested
• and evidence demonstrated in a way an auditor can actually follow

If you’re not building that alignment in from the start and your tests can’t be traced to regulatory principles or standards, you’re doing it wrong. You might still find defects, but you’re leaving the biggest risk that when the regulators, auditors (or lawyers) turn up and ask, “Show me how this system complies,” all you have is a pile of metrics and no coherent story.

This is where the AI testing industry is headed, not as an afterthought to reverse engineer answers for auditors, but as a core component of regulatory compliance. Testing is becoming part of an organization’s ability to justify why an AI system should exist at all.

Because when enforcement arrives as large parts of the world are intending, it won’t matter how clever you hoped you built your model but how well you can demonstrate that it is governed, monitored, and safe.