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Document workflows · Practical playbook

Prepare audit working papers for AI

Working papers connect procedures, source records, reviewer comments and conclusions. An AI writing task should not blur those links or carry client records into an unapproved environment. Removing the client name is only one part of preparing a useful, bounded excerpt.

For Audit operations and assurance teams

Synthetic example

Synthetic example: documenting an exception

A fictional audit team wants a clearer exception-note structure. The invented workpaper contains a procedure, observation and open follow-up. It is not a client engagement, audit finding or assurance outcome.

What you are working with

  • An XLSX working paper with sample items, account identifiers and evidence references.
  • A DOCX note separating the procedure performed from the observation.
  • Reviewer comments containing client contacts and links to source records.

A safer approach

  • Use invented sample items and evidence labels to preserve the documentation structure.
  • Keep the procedure, observation, exception and pending follow-up in separate fields.
  • Remove live source links and retain the original evidence in the engagement system.

Expected outcome: The assistant proposes clearer wording or headings without changing the evidential status of the exception. The engagement team verifies the text and remains responsible for the conclusion.

Put it into practice

Work through the procedure

  1. Choose a documentation task

    Define whether the request concerns structure, readability or a specific formula explanation. Confirm the engagement's permitted AI use and data destination. Do not let a wording request expand into asking the assistant to conclude on evidence it has not reviewed.

  2. Preserve traceability without identities

    Use stable synthetic reference labels across the procedure and observation. Include enough context to understand what was tested, but remove account identifiers, client contacts and live evidence links. Keep any mapping within the engagement workspace, outside the shared artifact.

  3. Review the working-paper package

    Inspect hidden sheets, reviewer comments, tracked changes, hyperlinks and document properties. Check that an exported excerpt does not retain unrelated sample selections or attached evidence. If you cannot separate the approved section cleanly, construct a new synthetic example instead.

  4. Compare the proposed wording

    Verify that every statement describes the work actually documented in the approved example. Preserve exceptions, limitations and incomplete follow-up. Review any proposed formula using local test cases, and do not replace original working papers with generated prose without the normal review process.

Evidence before approval

What to check before proceeding

1. Evidence references

Ready when
Each synthetic reference points consistently to the intended item.
If the check fails
Repair the reference chain before asking the assistant to rewrite it.

2. Conclusion boundary

Ready when
The output retains unresolved matters and does not imply additional procedures occurred.
If the check fails
Reject the unsupported language and restore the recorded status.

3. Source separation

Ready when
Original records, client identities and live evidence links remain in the engagement system.
If the check fails
Create a smaller approved excerpt and inspect it again.

Common mistakes to avoid

  • An assistant may replace cautious observation wording with a stronger conclusion that the documented work does not support.
  • A reference code can identify a client or account when it matches a shared filing convention; use synthetic labels for examples.
Workforce AI Security

Evaluate this workflow with Aona

Where Aona can help

Aona's configured prompt and file policies can be tested with synthetic workpaper identifiers in DOCX, XLSX or PDF. Verify the actual provider and browser/native route and review the produced artifact.

What to confirm

Aona does not establish audit evidence sufficiency, detect every engagement-specific code or provide assurance. Managed evaluation uses regional processing; the audit team's purpose and evidence controls remain necessary.

Managing client workpapers across an accounting team?

Review a synthetic workpaper, your AI tools and the file-handling behavior you need. Discuss supported controls and the manual checks that remain part of your review process.

FAQ

Questions about this workflow

A limited excerpt does not establish that all relevant evidence was reviewed. Keep the task focused on approved documentation assistance and retain professional conclusions within the engagement's review process.
Technical evaluation

Managing client workpapers across an accounting team?

Review a synthetic workpaper, your AI tools and the file-handling behavior you need. Discuss supported controls and the manual checks that remain part of your review process.

Redact Client Identifiers in Audit Working Papers | Aona AI