The audit is the discipline that turns a promising demo into a production surface a CFO, CIO, CISO, and audit committee can read.
Auditing an NL-to-SQL system means setting scope and materiality, replaying production-like questions against a golden dataset, checking dataset quality and permissions, logging exceptions, re-testing fixes, and issuing an audit memorandum with working papers.
Start with scope, not prompts.
A finance audit starts by defining which answer surfaces matter, who uses them, and which failures are material. Prompt tuning comes later.
Set scope
Name the product surface, tenants, personas, workflows, datasets, and model versions in scope.
Set materiality
Define thresholds by question tier, persona, and workflow before scoring begins.
Build working papers
Assemble the golden dataset, trace samples, dataset-quality checks, policy logs, and known exceptions.
Replay traces
Run end-to-end traces through the stack and score the final answer, not only intermediate agents.
Classify exceptions
Separate model, semantic, data, permission, rendering, and scope-limitation failures.
Issue the memorandum
Produce the opinion, thresholds, exceptions, remediation status, and appendix evidence.
Use audit categories the buyer already understands.
The goal is not a vanity score. The goal is a legible opinion on whether the system can be trusted in the scoped workflow.
OPINION | MEANING | PRODUCT ACTION |
|---|---|---|
Clean | Material questions pass above threshold. | Expand or keep operating with monitoring. |
Qualified | Named exceptions remain but scope can continue. | Remediate, re-test, and disclose limits. |
Adverse | Material failures make the surface unsafe for production. | Do not expand until fixes pass. |
Scope limitation | Data or access limits prevent a clean opinion. | Fix the evidence base before answering. |
The audit has to refresh when the system changes.
A static report goes stale as soon as the schema, prompt, model, policy, or tenant data changes. Finance teams need recurring and event-driven refreshes.
How to audit an NL-to-SQL system, answered plainly.
At minimum: product, engineering, data, security or risk, and the business owner for the finance workflow. Mature teams also involve internal audit or compliance.
Any finance team using natural-language SQL for operating numbers needs evidence. Regulation raises the stakes, but the operating risk exists either way.
The output should be an audit memorandum with an opinion, scope, materiality thresholds, exceptions, remediation status, and working papers.
Keep the evidence trail connected.
Audit memorandum template
The artifact structure behind the audit opinion.
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
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.