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Review an AI-assisted audit workpaper as audit evidence, not as a polished story produced by software. Before relying on it, establish the assertion and risk, identify the tool and configuration, prove that the input data is complete and accurate, reperform or test the procedure, challenge the output for omissions and bias, and preserve a chain from source records through logic and assumptions to the final conclusion. A reviewer must also document exceptions, contradictory evidence, consultations, preparer and reviewer identities, and review dates.
Human professional scepticism remains mandatory. If the tool’s logic cannot be explained or its results cannot be reproduced and tested, perform alternative procedures or consider whether the limitation affects the audit scope. The checklist below turns those principles into a review that an audit manager can sign off.
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What makes an AI-assisted workpaper acceptable audit evidence?
An acceptable workpaper lets an experienced auditor understand the purpose of the work, the source and reliability of the information, the procedure performed, the evidence obtained, the conclusion reached, and who prepared and reviewed it. AI does not change that evidential requirement. It changes the number of components that must be identified and controlled: model or rule configuration, prompts, retrieval context, transformations, generated output, and changes over time.
PCAOB AS 1215 describes audit documentation as “the written record of the basis for the auditor’s conclusions” and says it should be detailed enough to show its purpose, source, and conclusions. A narrative that merely says an AI system found no unusual items is not sufficient. The reviewer needs to see which population was analyzed, what the system did, what exceptions it returned, how those exceptions were investigated, and how the result supports the relevant assertion or control conclusion.
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- Purpose: the financial-statement assertion or control objective, assessed risk, population, period, and reason AI was used.
- Inputs: authoritative files or system extracts, permissions, extraction date, completeness checks, transformations, and relevant application or IT controls.
- Method: tool name, model or rule version, system settings, prompt or code, retrieval instructions, parameters, assumptions, and human preprocessing.
- Results: output files, logs, source references, formulas or code, exceptions, confidence information, and any manual edits.
- Conclusion: the reviewer’s evaluation of whether the evidence is sufficient and appropriate for the stated purpose.
What should I establish before reviewing the workpaper?
Start with the audit question, not the generated text. A clear objective gives the reviewer a basis for deciding whether the population, procedure, and output are relevant and precise enough.
Define the assertion, risk, and procedure objective
Write the assertion or control objective in operational terms, such as completeness of revenue, existence of inventory, valuation of receivables, or operating effectiveness of an approval control. Record the assessed risk, the population and period covered, the expected exception conditions, and the reason an AI-assisted procedure was selected instead of—or in addition to—a conventional test.
Specify what a successful result would and would not establish. An anomaly-ranking model may help select items for testing; it normally does not, by itself, prove that the remaining population is fairly stated. A summarization tool may organize contracts for review; it does not replace reading the underlying terms when those terms affect the conclusion.
Identify the tool and its configuration
Record the vendor or internal system, model or software version where available, date and time used, environment, account or permission level, and relevant system settings. Preserve the exact prompt, rule, query, code, retrieval instructions, sampling parameters, and any examples supplied to the system. Note human preprocessing, such as column renaming, file conversion, filtering, deduplication, or removal of records.
AI services can change without a visible change to the user interface. Capture the version or release identifier when the service provides one, and record the date of each material run. If the system is an internal model, document the model build, feature set, training or reference data, and deployment configuration to the extent available.
Define the reviewer’s evidence threshold
Before looking at the answer, decide what evidence is needed for the assertion and what level of precision is required. PCAOB AS 1105 requires sufficient appropriate evidence and testing of the accuracy and completeness of information produced by the company. It also requires evaluating whether that information is sufficiently precise and detailed for the audit purpose. These requirements apply when AI transforms or analyzes company information; an attractive interface does not lower the threshold.
How do I test whether the source data is complete and accurate?
Input reliability is the foundation of the entire workpaper. Tie the files used by the AI procedure to authoritative records, then test that the right records, fields, dates, and transformations reached the tool.
Reconcile the population to an authoritative source
- Identify the system of record and obtain an immutable or access-controlled extract where possible.
- Record the extraction date, period, filters, record count, control totals, and file or database identifiers.
- Reconcile counts and monetary totals from the extract to the general ledger, subledger, report, or other authoritative record used in the audit.
- Investigate differences caused by timing, currency, duplicate records, deleted items, status filters, joins, or excluded fields; document the resolution rather than silently adjusting the file.
- Retain a hash, read-only copy, or equivalent integrity control when firm policy permits, so a later reviewer can determine whether the analyzed file changed.
Test accuracy, completeness, and period
Select records from the input and agree key fields—amount, date, identifier, account, status, and relevant attributes—to source documents or the originating application. Test both ordinary and unusual records. Check that the period includes all transactions required by the audit objective and excludes transactions outside it. A file that is numerically complete but contains the wrong period is not reliable evidence.
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Check for nulls, truncated text, encoding errors, duplicate keys, unexpected data types, rounding, currency conversion, and lost leading zeros. Confirm that joins and calculated fields preserve the intended population. For an AI summary of contracts or invoices, verify that all pages, attachments, amendments, and relevant metadata were supplied; retrieval that silently omits a document creates a completeness risk.
Evaluate access, transformations, and controls
Document who extracted and transformed the data, which scripts or spreadsheet formulas were used, and whether those scripts were reviewed or version-controlled. Confirm that permissions allowed the user to see the full population and that row-level security did not exclude entities, locations, or periods. Consider relevant IT general controls and application controls when the reliability of the source depends on them.
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Keep the pre-transformation file, transformation steps, and post-transformation file or an equivalent reproducible record. If a person manually corrected a value, retain the original, the reason, the approver, and the date. Do not treat a model’s statement that it received a complete file as evidence of completeness.
How do I test whether an AI summary, classification, or anomaly result is accurate?
Reperform enough of the procedure to determine whether the output is accurate, complete, reproducible, and reasonable for the stated objective. A reviewer should independently calculate or inspect results rather than accepting the system’s confidence language.
Re-run a representative sample
Use a sample that covers ordinary items, high-value items, unusual formats, multiple entities or locations, period-end activity, and records near decision thresholds. Include cases that were not used as prompt examples or rule-development examples. For a classification, compare the AI label with a reviewer’s independent determination and investigate every disagreement. For an anomaly score, independently recalculate the relevant metric or apply a separate analytical procedure to determine whether the flagged condition is real.
Test edge cases and negative cases
Deliberately test missing fields, contradictory dates, zero or negative amounts, duplicate records, foreign-language text where relevant, amended documents, and records that should not trigger an exception. Include known exceptions and known clean items. A system that performs well on typical examples can still fail at the boundaries that matter most to an audit.
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Trace every material statement in a generated narrative to the underlying document or data field. Look for invented facts, unsupported explanations, omitted qualifiers, incorrect dates or amounts, and citations to passages that do not contain the claimed information. For extraction tasks, compare the output with a manually reviewed set to identify missed clauses, duplicate facts, and values copied from the wrong document.
Assess bias in the data and in the decision rule. A model may over-flag a business unit because historical investigations focused on that unit, or under-flag transactions whose descriptions use unfamiliar language. Automation bias is itself a review risk: ask whether a human would have reached the same conclusion without seeing the AI result, and document the answer for material judgments.
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Assess reasonableness against other audit evidence
Compare the output with confirmations, reconciliations, control testing, substantive tests, subsequent events, and discussions with management. An AI result that conflicts with reliable evidence requires investigation, not averaging. Determine whether the conflict is caused by a population difference, timing, a transformation error, an incorrect assumption, or an actual misstatement or control deficiency.
How can I prove that an AI conclusion is traceable to source evidence?
Traceability is a preserved chain, not a claim that the model is explainable. A reviewer should be able to move from the final conclusion back through the output, logic and assumptions, transformed data, and original evidence without relying on memory or an opaque chat history.
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|---|---|---|
| Source | Authoritative file or system extract, period, record counts, control totals, permissions, and integrity identifier | Agree the population and key fields to the system of record; investigate missing or changed records |
| Preparation | Cleaning scripts, formulas, filters, joins, mappings, manual edits, and before-and-after files | Reperform transformations and verify that they do not alter the intended population or amounts |
| Logic | Prompt, rule, code, model or version, parameters, retrieval context, assumptions, and examples | Determine whether the logic addresses the assertion and test conditions outside the development examples |
| Run | Date, environment, input identifier, output, logs, errors, and change history | Re-run a controlled sample and explain any difference in results |
| Interpretation | Human review notes, exception evaluation, consultations, and links to supporting documents | Check that each material statement is supported and that contradictory evidence was resolved |
| Conclusion | Final conclusion, preparer and reviewer names, review dates, sign-off, and cross-references | Verify that the conclusion follows from sufficient appropriate evidence and is linked to significant findings |
For retrieval-augmented systems, preserve the retrieved passages or document identifiers, retrieval settings, and the version of the source repository. For code or formulas, retain the exact code, libraries or dependencies where material, parameters, and execution output. If the interface cannot export a durable log, create a contemporaneous record that captures the same information and explain the limitation.
What should I do when the AI result conflicts with other evidence?
Contradictory findings are a signal to expand the work, not to choose the more convenient answer. Use a documented escalation path.
- Describe the conflict precisely, including the records, amounts, dates, assertions, and evidence involved.
- Check population boundaries, period cutoffs, filters, joins, units, currency, and transformation steps for a technical explanation.
- Reperform the relevant calculation or inspect the original documents independently of the AI output.
- Consult the appropriate accounting, auditing, data, or technology specialist when the issue involves complex logic or a significant judgment.
- Perform additional procedures, such as expanding the sample, obtaining external evidence, or testing the control that generated the data.
- Record the revised conclusion, unresolved exception, effect on assessed risk, and effect on the audit opinion or control report.
Do not delete an earlier output that was wrong. Retain the original, mark it as superseded, and link the correction and rationale. This change history helps the reviewer determine whether the final conclusion was reached after an appropriate response to the exception.
What must an audit manager check before signing off?
The manager’s review should address both the ordinary audit requirements and the additional risks introduced by AI. Use the following sign-off sequence.
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- Input reliability: Verify reconciliation to authoritative records, completeness and accuracy tests, period checks, permissions, and transformation controls.
- Procedure reproducibility: Confirm the prompt, rule, code, model or version, settings, assumptions, and run date are retained, and that a sample was reperformed.
- Output testing: Review edge cases, known exceptions, negative cases, omissions, hallucinated facts, classification errors, bias, and reasonableness against other evidence.
- Traceability: Follow material conclusions back to source records, retrieved passages, formulas or code, parameters, and human interpretation.
- Exceptions: Ensure contradictory evidence, consultations, additional procedures, revised conclusions, and unresolved matters are documented.
- Security and privacy: Confirm confidential client data was handled under firm policy and applicable privacy requirements, including whether an external service transmitted data outside approved boundaries.
- Sign-off: Record preparer and reviewer identities, review dates, review notes, final conclusion, and cross-references to supporting workpapers and significant findings.
AS 1215 requires documentation to make the purpose, source, procedures, evidence, conclusions, significant issues, preparer, reviewer, and review dates understandable. A sign-off that merely says reviewed or agrees does not show that these elements were evaluated.
How do PCAOB, IAASB, and AUASB guidance apply?
The applicable auditing standard depends on the engagement and jurisdiction. The following sources describe the core expectations relevant to AI-assisted workpapers.
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| Source | Practical implication for review |
|---|---|
| PCAOB AS 1215, Audit Documentation | Documentation must show the basis for conclusions, purpose, source, procedures, evidence, significant issues, preparer, reviewer, and dates, with a clear link to significant findings. |
| PCAOB AS 1105, Audit Evidence | Information produced by the company must be tested for accuracy and completeness, and evaluated for sufficient precision and detail for the audit purpose. AI-transformed information is not exempt. |
| PCAOB technology-assisted analysis amendments announced June 12, 2024 | The clarified responsibilities are intended to reduce the risk of issuing an opinion without relevant and reliable evidence. Technology assistance does not transfer the auditor’s responsibility to the tool. |
| IAASB 2020 material on automated tools, robotics, analytics, and AI | Provides documentation considerations under ISA 230, but is non-authoritative. Use it to inform implementation while applying the requirements of the standards governing the engagement. |
| AUASB 2025 guidance on AI’s impact on auditors | Calls for review of data, currency, bias, reliability, assumptions, model logic, transparency, changes, output reasonableness, test checks, professional scepticism, security, and documentation. It states that AI tools are not a substitute for human judgement. |
These sources do not provide a universal pass rate for AI workpapers. No general published accuracy statistic establishes that a particular model or workflow is reliable across audits. Reliability must be demonstrated for the specific data, procedure, population, period, and conclusion.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Which review approach is stronger: narrative review or evidence-centered re-performance?
A narrative-only review is faster but leaves major evidential gaps. An evidence-centered re-performance takes more effort and creates a defensible record of how the conclusion was obtained.
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|---|---|---|
| Source-data testing | Accepts the uploaded file or a system statement about completeness | Reconciles to authoritative records and tests completeness, accuracy, period, permissions, and transformations |
| Reproducibility | Reads the displayed answer; rerun conditions may be unknown | Preserves inputs, version, settings, prompt or code, date, and reruns representative cases |
| Audit-trail completeness | Keeps a final report or screenshot | Keeps source identifiers, logs, retrieved passages, formulas or code, assumptions, changes, exceptions, and sign-off |
| Logic and assumptions | Relies on a general vendor description or confidence score | Tests the actual rule, prompt, parameters, assumptions, and conditions outside development examples |
| Output accuracy | Checks a few plausible examples | Uses representative, high-risk, edge, negative, and known-exception cases with independent recalculation |
| Exception handling | Removes or explains unusual results informally | Documents contradictory evidence, consultations, additional procedures, resolution, and effect on the conclusion |
| Reviewer effort | Lower initial effort, higher risk of rework or unsupported reliance | Higher initial effort, stronger basis for reliance and review sign-off |
| Security and privacy | Assumes the service is approved | Checks policy, data location or transmission, access, retention, confidentiality, and external-subprocessor implications |
| Change control | Assumes repeated runs are equivalent | Records model, rule, data, prompt, dependency, and output changes and assesses their effect |
| Alignment with standards | May not demonstrate sufficient appropriate evidence or documentation | Directly supports the evidence, documentation, precision, and professional-scepticism requirements applicable to the engagement |
How should security, privacy, and change control be reviewed?
Before client data enters an external or shared AI environment, confirm that the tool and workflow are approved under firm policy and applicable privacy requirements. Determine whether prompts, uploaded files, outputs, or feedback are retained, used for model improvement, accessible to other users, or transmitted across jurisdictions. Use masking, minimization, access controls, and approved storage where required, and document any residual risk.
Change control matters because a model, retrieval index, prompt template, dependency, or source file can change the result without changing the workpaper title. Record the version and run date, compare material reruns, and investigate unexplained output changes. If a service has no stable version identifier, preserve the configuration and output and state that reproducibility is limited.
What if the model’s logic is proprietary or cannot be explained?
Proprietary logic does not automatically make a procedure unusable, but it limits what the auditor can establish. Obtain available documentation, test results, validation information, access controls, change notices, and independent evidence about the specific function used. Test outputs directly and avoid relying on claims that cannot be verified.
If material assumptions, decision rules, or changes cannot be understood or tested, design alternative procedures that provide sufficient appropriate evidence. If no alternative can address the risk, evaluate whether the limitation creates a scope restriction or affects the audit opinion. Document that judgment and the reasons for not relying on the opaque component.
Can an AI confidence score prove that a workpaper is accurate?
No. A confidence score is an output of the system, not independent audit evidence, and its calibration may not match the engagement’s risks or error costs. Treat it as a triage signal only, then verify the underlying records, assumptions, and procedure with human-designed test checks.
How should teams retain prompts and model outputs?
Retain the exact prompt or rule, system settings, input identifier, model or version, run date, output, error messages, and any human edits in the engagement file or an approved linked repository. Preserve enough context for an independent reviewer to understand what the system saw and why it produced the result. Apply the same retention, access, and confidentiality controls used for other audit documentation.
What should happen when the original input file is unavailable?
Do not recreate it silently. Identify the authoritative source, document why the original cannot be obtained, assess whether a replacement extract is equivalent, and perform additional completeness and integrity testing. If equivalence cannot be demonstrated, reduce reliance on the AI procedure and obtain alternative evidence.
How much of an AI-generated summary must the auditor verify?
Verify every material statement, number, date, condition, and conclusion that the summary supports, not just a convenient sample of sentences. The extent of testing should reflect the risk and the summary’s role: a document index may require different checks from a summary used to conclude on a significant estimate. Keep the underlying passages or records that support the verified statements.
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Who is responsible for an AI-assisted audit conclusion?
The engagement team and its designated auditor remain responsible for the conclusion; responsibility cannot be delegated to a model or service provider. Preparers should document how they performed the work, and reviewers should document their review and resolution of issues. Specialists may assist with data or model evaluation, but the auditor must evaluate whether the resulting evidence is sufficient and appropriate.
Frequently Asked Questions
Can an AI tool select my audit sample without further testing?
It can help identify or rank items, but the sampling method, population completeness, selection parameters, and coverage must be documented and evaluated against the audit objective. Test that the selection logic did not exclude relevant strata or alter the intended sampling risk.
Should I keep a screenshot of the AI chat as the workpaper?
A screenshot can supplement the file but rarely captures the source data, version, transformations, retrieved context, or change history needed for reproduction. Keep structured exports and linked evidence, then use a screenshot only to show an interface state that cannot otherwise be preserved.
How do I review an AI procedure run by a service center or another audit team?
Obtain the same purpose, population, configuration, input-reliability tests, output checks, exception log, and sign-off evidence you would require from your own team. Confirm who performed and reviewed the work, when it was done, and whether your engagement’s standards and data-security requirements were applied.
What is the best response when an AI result is plausible but unsupported?
Treat it as an unsubstantiated lead, not a conclusion. Trace it to source evidence, perform an independent procedure, and document the result; if support cannot be obtained, remove the claim from the conclusion and assess whether more work is necessary.
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