An AI assessment can read well and still create more work than it removes. The reviewer has to check whether the findings are supported, whether anything important was missed, and whether the evidence covers the service and period being assessed. In GRC, that verification work is part of the product’s performance.
A single accuracy percentage hides too much. Extracting a policy date, identifying a control exception, and recommending a risk response are different tasks with different consequences. Before asking how accurate a model is, define the decision the workflow supports and what would count as a material error. That gives the evaluation a unit of work people can inspect.
Start with the control question, not the model label
Consider an illustrative vendor review: an assurance report covers one service, while the assessment concerns another. The company name matches, and the cited passage may be accurate, but the conclusion still needs evidence for the service being purchased. NIST’s control assessment guidance is a useful reference for defining the objective and scope before reaching a conclusion.
The appropriate output identifies the scope mismatch and the evidence still needed. It should not apply one service’s assurance to another or treat missing coverage as proof that the vendor failed a control. This is where domain knowledge matters: applying the right test to the evidence available. “Purpose-built” is a product description; the test shows whether that knowledge is present in the workflow.
Measure misses and unsupported findings separately
Build an evaluation set with expected findings agreed by qualified reviewers. Count material issues the system misses separately from findings the reviewer rejects as unsupported. For each proposed finding, inspect whether the cited passage supports the conclusion and covers the right entity, service, and period. Report the denominator with each measure: five rejected findings out of six means something different from five out of five hundred. Record severity too. A critical exception should not be averaged away by many correct low-consequence answers.
Confidence needs evidence of its own
A source link and a confidence score serve different purposes. The link lets a reviewer check the conclusion; the score expresses an estimate that needs validation. Ask how scores correspond to observed correctness on representative cases held out from tuning. Even a well-calibrated score across a dataset does not prove one particular finding. Reviewers need to see missing information and conflicting evidence, not just a number beside the answer.
The workflow is what the buyer needs to trust
A correct finding is useful only if it reaches the right review and retains its evidence. Anthropic’s agent evaluation guide separates a trial’s transcript from its final outcome and recommends multiple trials because results can vary. Apply that to GRC: inspect the saved assessment, source references, approval, and downstream task. Repeat important cases. A confident message saying “complete” is not the same as a completed, reviewable assessment.
Use a small set of difficult cases
Alongside an ordinary successful case, include a missing report, conflicting policy versions, an exception buried in otherwise reassuring evidence, and a failed collection. Add a permission refusal and an interrupted update if the agent can act in other systems. Write the expected behavior before running the test. Use authorized or redacted material, preserve the inputs, and keep some examples out of the setup process.
The result should distinguish what was established from what remains unknown. For an action, it should also show whether the requested change happened, whether permission was enforced, and whether a retry preserved one consistent record. Have a reviewer check the test cases and the grading logic; an automated score is only as useful as the criteria behind it.
Count the work needed to accept the result
Measure how long a reviewer spends finding sources, checking conclusions, correcting errors, and following up on missing information. Compare that total with your current process on equivalent work. A faster draft with a longer verification step may be useful in some workflows and a poor trade in others. Name the tradeoff rather than reporting generation speed as a finished assessment.
At Zania, this is the standard we want buyers to apply to the workflow they intend to use. Bring representative evidence, define a material error, and ask us to show the result and the review that remains. The 12-question AI GRC evaluation covers the broader buying decision; the AI TPRM pilot checklist applies the same discipline to vendor assessments.
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