Useful audit fields include source event, capture timestamp, AI suggestion, confidence, model or rule version, user edit, approver, approval timestamp, exception, release timestamp, and downstream transfer status.
Traceability from Source to Reviewed Entry
Preserve the original suggestion, field changes, and source evidence so reviewers can follow the record.
Audit component | What to check or do |
Preserve the Original | Do not overwrite the original suggestion without history. The firm should be able to compare source activity, initial AI output, user changes, and final approved entry. |
Field-Level History | Track changes to client, matter, duration, billing status, narrative, task code, and activity code. Field-level history is more useful than a generic 'edited' flag. |
Source Traceability | Where practical, retain a reference to the underlying calendar event, email, document, meeting, browser activity, or manual note. Access should respect existing permissions. |
Approval History
Record who approved the entry, when, which warnings existed, which exceptions were granted, and whether the entry changed after approval.
Rule Version
When client rules change, the audit should show which version applied at the time. Historical billing periods may need the earlier rule set.
Model Version
If a generative model creates narratives or code suggestions, firms may want to record model family, configuration, prompt or policy version, and material change date.
Failed Transfers
Audit status should distinguish released, pending, failed, rejected downstream, corrected, and resent. This helps prevent duplicate release.
Privacy and Retention
Audit logs can contain sensitive metadata. Define retention, access, export, deletion, security, and legal-hold requirements.
Use Audit Data for QA
Audit trails can support matter correction rate, duration correction rate, narrative edit rate, code correction rate, exception rate, and review-time metrics.
Use Audit Data for Training
Repeated edits can show where AI suggestions are weak, client rules are unclear, matter data is poor, users need examples, or integrations fail.
How MIRA Fits
MIRA's review workflow allows users to correct and approve captured time before release. Firms evaluating MIRA or any AI timekeeping product should confirm the audit fields, retention, export, and change-history controls available in their deployed configuration.
Related Resources
Authoritative References
This guide is for educational purposes only. Billing, pricing, accounting, AI, privacy, client, and professional-conduct requirements vary by firm, engagement, and jurisdiction.
Frequently Asked Questions
Why do AI time entries need audit trails?
Because generated fields can be corrected, overridden, or approved, and firms may need to understand how the final record was created.
Should the original AI suggestion be preserved?
Where appropriate, yes, along with user edits and final approval.
Can audit logs contain confidential information?
Yes. Access and retention should be controlled.
What should a buyer ask a vendor?
Ask about source traceability, field history, model and rule versions, export, retention, permissions, and downstream status.
