The dashboard is not the deliverable. The memorandum is the buyer-readable artifact that turns eval evidence into an operating decision.
An AI audit memorandum is a structured evidence artifact that summarizes the opinion, scope, materiality thresholds, exceptions, remediation status, and working papers behind an AI system during a defined review period.
The first three pages should be executive-readable.
The memorandum should let a CFO, CIO, CISO, audit committee chair, and external auditor read different depths from the same evidence trail.
Page 1: Opinion
Clean, qualified, adverse, or scope-limited, with period covered and system surface in scope.
Page 2: Scope and materiality
Personas, tenants, workflows, question tiers, thresholds, and any changes during the period.
Page 3: Exceptions
Material findings with trace evidence, root cause, owner, remediation, and re-test status.
Appendix: Working papers
Golden-set results, dataset-quality checks, drift triggers, CI failures, optimizer history, and red-team traces.
Use discrete categories instead of vague confidence.
A memorandum should not say the system is generally strong. It should name the opinion and the exceptions that support or limit it.
CATEGORY | WHEN TO USE IT | WHAT TO DISCLOSE |
|---|---|---|
Clean | Material evidence passes threshold. | Scope, period, thresholds, and monitoring cadence. |
Qualified | A limited set of exceptions remains. | Exception list, owner, remediation, and re-test date. |
Adverse | Material failures make the surface unsafe. | Root causes and rollout block. |
Scope limitation | The evidence base is insufficient. | Missing data, access, or quality control needed for an opinion. |
One memorandum should serve multiple readers.
The same memo should give executives the operating read and give auditors the working-paper trail. That is the point of making eval evidence structured.
CFO
Reads whether the system can support finance operating decisions.
CIO
Reads what improved, what regressed, and where engineering should invest.
CISO
Reads permissions, policy, red-team, and incident evidence.
Audit committee
Reads opinion, materiality, exceptions, and cadence.
The audit memorandum template for AI systems, answered plainly.
It can support compliance, but it is broader. It is the operating evidence artifact that explains whether the AI system can be trusted in the scoped workflow.
Refresh it on a defined cadence and after material changes such as model swaps, prompt changes, schema migrations, permission changes, or material incidents.
A dashboard shows current signals. A memorandum interprets the evidence into scope, opinion, exceptions, remediation, and working papers that decision-makers can review.
Keep the evidence trail connected.
How to audit an NL-to-SQL system
The workflow that feeds the memorandum.
NL-to-SQL evals for finance
The canonical guide that ties answer correctness, dataset quality, golden sets, drift gates, and audit memoranda together.
AI Audit checklist
The inventory and evidence questions that shape the audit scope.
AI Audit
The two-week operating read that turns production AI behavior into board-readable evidence.
If a finance AI answer can move an operating decision, the evidence behind it needs to be readable after the answer is gone.
Bring one workflow, vendor, or AI portfolio. We will map the evidence needed for finance leaders to fund, ship, or stop it.