Your eval suite is the audit trail compliance wishes it had — if you design it that way. Eval-driven compliance turns your tests into the evidence that the model does what it claims, every release.
Most teams keep compliance and evals in separate rooms: evals for engineering, compliance for audits. Eval-driven compliance collapses them — the eval suite, designed right, is the evidence compliance needs that the model behaves as claimed.
The Conduct Rule: Evals as Evidence
An eval is a structured claim: "the model does X on data Y, with result Z." Designed right, that's exactly what compliance needs — evidence, per release, that the model's behavior is what was claimed. The eval becomes the audit trail.
- Evals as evidence of behavior, per release.
- Results retained, so past behavior is reconstructable.
- Failures gating the release, so a regression can't ship.
The Measurement Frame
Process measurement: the eval suite run per release, with results retained, is the compliance record. Compliance doesn't need a separate "audit" — it needs the eval results, the model version, the data version, and the release decision. The eval suite, versioned and retained, is that record.
- Eval results per release.
- Model and data versions attached.
- Release decision (ship / hold / fix) recorded.
What to Build
- An eval suite that covers the model's claimed behaviors.
- A release process that runs the suite and gates on it.
- Retained results, with model and data versions, as the audit trail.
- A fail -> fix -> re-eval loop, not a fail -> ship-anyway loop.
What to Refuse
- Evals without retained results.
- A release process that doesn't gate on evals.
- Compliance evidence that's a separate, manual artifact.
- Evals that don't cover the model's claimed behaviors.
Conclusion
Eval-driven compliance turns your eval suite into the audit trail — evidence per release that the model behaves as claimed. Run the suite, gate on it, retain the results with versions, and compliance gets the evidence it needs without a separate audit.
About FACTA
FACTA helps startups and growth-stage teams turn AI into production systems that keep running — not demos that impress once.
We design the architecture around the parts that actually break under real usage: tooling you own, credentials you control, failover, cost controls, observability. The boring infrastructure that keeps a system alive after launch.
Led by Matías Baglieri and Carolina Fogliato, we focus on one thing:
AI leadership that builds. Not just advises.
Ask us whether your eval suite could serve as a compliance record.
We'll tell you what it's missing. See the model audit checklist for what it covers.
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