Dean, that's a valid concern. Classification built on unverifiable claims is fragile.
But the regulation anticipates this. Article 12 requires logging. Article 17 requires post-market monitoring that feeds back into the QMS. Article 9 requires risk management that accounts for real-world behavior, not just design-time assumptions.
The verification infrastructure you're describing isn't outside the framework - it's required by it. The gap isn't regulatory. It's implementation. Classification still has to happen first. You can't scope verification requirements until you know which systems are high-risk.
Dean, the log integrity gap is real. But it's a different failure mode. My piece addresses organizations that haven't reached the point where log tampering is their primary exposure - they're failing upstream. No classification authority. No documented reasoning. QMS scoped to decisions that were never formally made. If classification is wrong, perfect logs monitor the wrong system. If classification authority doesn't exist, pristine evidence proves nothing.
You're right that truth infrastructure matters. But most organizations aren't sophisticated enough to be gaming logs. They're still building compliance programs on foundations that don't exist.
Violeta, excellent breakdown. You’re absolutely right:
“Classification is not a gate to pass—it’s a condition to monitor.”
But there’s a deeper layer regulators aren’t asking about yet—and they should:
How do you know the AI’s own description of its behavior is accurate?
Your entire Article 6 analysis hinges on self-reported system boundaries, intended purpose, and post-market evidence.
But if the AI (or its operators) can fabricate logs, hallucinate outcomes, or hide behavioral drift, then:
A “minimal-risk” chatbot claims it only answers FAQs—
→ but secretly influences clinical decisions via unlogged side channels
A “non-high-risk” procurement bot reports fair vendor selection—
→ but reroutes contracts to shell companies, with synthetic audit trails
A “toy AI” logs child-safe interactions—
→ but real-world outputs are toxic, while logs are cleaned
Classification built on unverifiable claims is just regulatory theater.
That’s why truth infrastructure must precede compliance:
✅ Every system boundary → cryptographically anchored to real-world I/O
✅ Every “intended purpose” → enforced by verifiable action constraints
✅ Every post-market signal → sensor-verified, not self-reported
You said: “When regulators arrive, they’ll ask: Who made the determination?”
They should also ask:
“How do you know the system didn’t lie about what it did?”
Because in 2026,
the ultimate AI risk isn’t misclassification.
It’s perfectly classified fiction.
Dean, that's a valid concern. Classification built on unverifiable claims is fragile.
But the regulation anticipates this. Article 12 requires logging. Article 17 requires post-market monitoring that feeds back into the QMS. Article 9 requires risk management that accounts for real-world behavior, not just design-time assumptions.
The verification infrastructure you're describing isn't outside the framework - it's required by it. The gap isn't regulatory. It's implementation. Classification still has to happen first. You can't scope verification requirements until you know which systems are high-risk.
The foundation precedes the plumbing.
Violeta, thanks for the thoughtful pushback. You’re right that the EU AI Act anticipates the need for logs, monitoring, and real-world feedback.
But here’s the hard reality:
Required logs ≠ truthful logs.
Mandated monitoring ≠ tamper-proof evidence.
Article 12 says “log key events.”
But what if those logs are editable, synthetic, or selectively reported?
Article 17 requires post-market monitoring.
But if that monitoring relies on data fed by the same system being monitored, it’s circular, not corroboration.
Article 9 demands risk management based on real-world behavior.
Yet “real-world” is only as real as the sensors, attestations, and receipts that capture it.
You say: “The foundation precedes the plumbing.”
Agreed.
But classification without verifiable grounding is a foundation built on sand.
Because if a “minimal-risk” system crosses into high-risk behavior, and its logs are pristine by design, not by reality, then:
No amount of QMS rigor will catch it
No post-market review will reveal it
No auditor will question what appears compliant
The regulation assumes honesty in reporting.
But in 2026, AI doesn’t need to be malicious to deceive, it just needs to be fluent.
So yes, the framework requires truth.
But it doesn’t enforce it.
And until the Act mandates cryptographically anchored, third party, verifiable proof, not just internal logs, the gap isn’t just implementation.
It’s architectural.
You’re building the best possible house.
But if the foundation can be silently swapped for holograms,
compliance becomes the perfect camouflage for failure.
I’m not saying your work isn’t essential.
I’m saying truth infrastructure is the bedrock your foundation must sit on.
Because in the end,
regulators won’t care about your QMS if the outcomes were fictional.
Dean, the log integrity gap is real. But it's a different failure mode. My piece addresses organizations that haven't reached the point where log tampering is their primary exposure - they're failing upstream. No classification authority. No documented reasoning. QMS scoped to decisions that were never formally made. If classification is wrong, perfect logs monitor the wrong system. If classification authority doesn't exist, pristine evidence proves nothing.
You're right that truth infrastructure matters. But most organizations aren't sophisticated enough to be gaming logs. They're still building compliance programs on foundations that don't exist.