Wants to show a regulator, per decision, how policy was applied — not reconstruct it afterwards.
Compliance · EU AI Act
Not a guideline on paper, but policy the control plane enforces: prohibited use blocked, high risk audited, every decision immutably recorded.
Wants to show a regulator, per decision, how policy was applied — not reconstruct it afterwards.
Has AI policy on paper and wants it enforced in practice, not left to whether people read it.
Wants prohibited and high-risk use caught at the gate, not discovered afterwards.
The problem
Prohibited applications and high-risk use must be handled demonstrably. Without a log per decision, there is nothing to show a regulator.
Numaga checks every prompt against risk class and data sensitivity before the model call: prohibited use is blocked, high risk carries audit and validation evidence, limited risk gets transparency labels. Policy rules, not LLM judgement: reproducible for inspectors.
Risk-aware routing & data classification
Not as a guideline, but as policy, enforced on the control plane.
| EU AI Act class | Platform behaviour |
|---|---|
| Prohibitede.g. social scoring | Blocked on the control plane. |
| High riskdecision & process support | Audit · validation evidence. |
| Limited risktransparency required | Transparency labels added in-line. |
| Minimal risk | Standard routing with monitoring. |
| Sensitivity | Behaviour |
|---|---|
| Public | Standard routing. |
| Internal | Identity + audit. |
| Confidential | EU-compliant provider + redaction. |
| Strictly confidentialpersonal data, trade secrets | Strictest provider class + redaction + long-term retention. |
In practice
Numaga is running a pilot with Ontdek Zorg, a home-care organisation in Gelderland. Healthcare, with special-category personal data under the GDPR, is where responsible AI is hardest. That is exactly where we put the control plane to the test.
In a demo environment we show how a prohibited prompt is blocked, and the audit trail it produces.
Request demo access