A model audit isn't a vibe check — it's a structured review that lets compliance sign off before the model ships. Here's the checklist that actually works.
A model audit is the compliance gate that, done well, lets a model ship. Done badly, it's a rubber stamp that gets everyone in trouble later. The checklist is what makes it the first.
The Diligence Frame: What Compliance Needs
Treat the model audit like diligence — the structured questions that expose whether the model is safe to ship. Compliance needs answers to specific questions, not assurances about quality. The checklist is the set of questions.
- What does the model do, and on what data?
- What are the known failure modes?
- What's the audit trail — can a decision be reconstructed?
- What's the kill switch and the escalation path?
- Who owns the model in production?
The Checklist
- **Intended use.** Documented, specific, and bounded.
- **Data.** What it trained on, what it sees at inference, what's retained.
- **Evals.** A repeatable test suite, with the results.
- **Failure modes.** Known, documented, with mitigations.
- **Audit trail.** Decisions logged and reconstructable.
- **Guardrails.** Confidence thresholds, escalation, kill switch.
- **Ownership.** A named owner in production.
- **Review.** A cadence for re-audit.
What to Refuse
- "We tested it" without the eval suite.
- "It's mostly accurate" without the failure modes.
- "We'll add the audit trail later."
- No named owner.
How to Use the Checklist
Run the checklist before the model ships, not after. Each item is a gate — a model that fails an item doesn't ship until the item is closed. The checklist is what makes the audit a gate instead of a rubber stamp.
Conclusion
A model audit is a structured diligence review, not a vibe check. Run the checklist — intended use, data, evals, failure modes, audit trail, guardrails, ownership, review — before the model ships, and compliance can sign off without signing away the risk.
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.
Run your next model against the checklist above.
Tell us which items you can't close, and we'll help you close them. See eval-driven compliance for the eval-as-audit-trail pattern.
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