Answer the checklist by category
Mark each of the 15 audit criteria — grouped into data handling, human oversight, fairness/bias, incident response, model governance, logging/observability, and transparency — as Yes, Partial, or No.
AI ENGINEERING & SECURITY MVP
AI Audit Readiness
Answer a checklist of common AI compliance/audit criteria — data handling, human oversight, bias testing, incident response, model governance, logging, and transparency — grouped by category, and get a weighted readiness score with a gap breakdown before you launch.
- Weighted scoring across 7 audit categories
- Highlights your top gaps, worst first
- All answers stay in your browser
Answer the checklist and click Calculate readiness score to see your tier and gap breakdown.
0
out of 100, weighted across all categories
How it works
Each answer is weighted and scored
Higher-impact criteria (e.g. human review of consequential outputs, bias testing, pre-launch evaluations) carry more weight than lower-impact ones. Yes earns full credit, Partial earns half, No earns none.
See your tier and prioritized gaps
The weighted total becomes a 0–100 readiness score and a tier from Not audit-ready to Audit-ready, plus a per-category breakdown and the five highest-weight gaps to close first.
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Frequently asked questions
What compliance standard is this checklist based on?
It reflects criteria commonly requested in AI governance and audit reviews (data handling, human oversight, bias testing, incident response, model versioning, logging, disclosure) rather than one specific regulation. Treat it as a practical pre-launch checklist, not a certification against a named framework like SOC 2 or the EU AI Act.
How is the score weighted?
Each of the 15 items has a weight from 1–3 based on typical audit impact (e.g. an incident response plan and bias testing are weighted 3; disclosing known limitations is weighted 1). Your score is the sum of earned weight divided by total possible weight.
What does "Partial" mean for an item?
Use Partial when the practice exists but is incomplete, undocumented, or inconsistently applied — for example, logging exists for some but not all requests. Partial earns half credit for that item.
Is a high score a guarantee we’ll pass a real audit?
No. This is a self-assessment planning tool to surface obvious gaps before a real audit, legal review, or compliance sign-off — not a substitute for one.
Does this save my answers anywhere?
No. All scoring happens client-side in your browser from the checklist state on the page; nothing is uploaded or stored on a server.
Can I use this for a non-regulated AI feature too?
Yes. Even outside a formal audit, the checklist is a reasonable pre-launch bar for any production AI feature — good practice for reliability and trust regardless of regulatory scope.
How We Compare
| Feature | MVPHub | Internal compliance spreadsheet | General-purpose AI assistant |
|---|---|---|---|
| Weighted scoring across audit categories | Included | Limited | Not included |
| Prioritized, highest-impact gaps first | Included | Not included | Limited |
| Transparent, inspectable calculation | Included | Included | Not included |
| Runs instantly with no setup | Included | Included | Included |
A compliance spreadsheet can hold the same checklist but leaves prioritization to whoever reads it; a general-purpose assistant can discuss the criteria but won’t apply the same weighted rule consistently every time. MVPHub applies one transparent weighting to every answer.
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