AI Assurance Receipts Pack™
Stop accepting AI assurance claims without knowing what evidence should exist. Six receipts. One evidence standard. A defensible answer to: “What proof did we rely on?”
Someone says the AI system has been assessed. What evidence would allow you to believe them?
The problem is not document scarcity. It is evidence acceptance. Teams can produce policies, model reports, vendor packs, and test results without agreeing on what proof is sufficient for the risk decision in front of them. The result is an assurance claim that sounds stronger than the evidence underneath it.
What teams often have
Documents, screenshots, assessments and control evidence. Enough material to say something was reviewed, but no shared basis for deciding whether the evidence is sufficient.
What the buyer actually needs
An evidence acceptance standard. What should exist, how good it must be, when it becomes stale, what gaps remain, and who accepts the residual risk.
Six receipts. One evidence standard.
The value is not another checklist of documents. The pack gives you a repeatable way to decide whether evidence can actually support a risk acceptance, approval, or assurance conclusion.
A small sentence can hide a large governance gap.
“The model validation is still in date.”
Now test the statement. The customer population has changed since validation. The pack helps you ask whether the old result still supports the current risk decision, identify what must be refreshed, and record whether the decision can proceed, proceed with conditions, or wait for new evidence.
Start with one real decision.
You do not need to redesign the entire AI governance model before these tools become useful.
1. Define the decision
Start with the risk acceptance or approval decision, not with a folder of evidence.
2. Set and test the evidence standard
Use the receipts, minimum evidence matrix and 6C scorecard to determine what is sufficient and what is weak.
3. Close the gap visibly
Record missing evidence, currency triggers, ownership, and the final risk acceptance basis.
Built to be used in the room, not admired in a folder.
Leave the review knowing what can be relied on.
The pack gives assurance, risk and governance teams a repeatable way to distinguish evidence that merely exists from evidence that can carry a decision.
Free templates can tell you what to collect. This helps you decide what to accept.
The paid value is the evidence discipline behind the pack: a minimum standard, a quality test, a currency test, visible gaps, and a recorded risk-acceptance basis. The outcome is not “we have the documents.” It is “we can explain what proof we relied on, and why it was enough.”
Evidence → Challenge → Decision.
Each product stands alone. Together they form a simple decision-assurance sequence.
Define what evidence is sufficient before risk is accepted.
Know what to ask, what proof to request, and where to challenge.
Take one use case to a recorded, evidence-backed decision.
A few sensible questions.
Does this replace our AI governance framework?
No. Keep your existing policies, standards and control framework. The pack sits closer to the decision and turns those requirements into a practical evidence standard.
Do we need a mature AI governance programme first?
No. Start with one material AI risk acceptance or approval decision and use the pack to improve the evidence discipline around it.
Is this only for Internal Audit?
No. Internal Audit can use it, but so can Risk, Compliance, AI Governance, and GRC teams that need a common evidence language.
What happens when evidence is still formally current but the system has changed?
Treat material change as an evidence currency trigger. The pack makes that distinction explicit rather than relying only on elapsed time.
Why pay for this when evidence checklists exist for free?
Because the hard part is not naming documents. It is deciding what evidence is sufficient, how current it must be, what gaps matter, and what basis supports risk acceptance. The pack is designed as an evidence acceptance standard rather than a document list.
Six receipts. One evidence standard.
Stop accepting AI assurance claims on the strength of document existence alone. Leave with a defensible answer to: “What proof did we rely on?”