AI governance
Audit-trail and accountability controls for AI-assisted records review
This page is for regulated operators, CAMOs, repair stations, and records teams deciding how AI-assisted review can fit into controlled airworthiness records work. EE reviews governance, source retention, reviewer accountability, audit trail, data handling, exception approval, and system-use boundaries. The output is a governance gap list and control model showing what AI can assist with and what remains a human accountable decision.
When this review is needed
- The file arrives with a deadline tied to Internal approval of an AI-assisted process.
- Several record classes need to be checked together.
- Source documents are present but the index is not trusted.
- The team needs findings ranked by decision impact.
The problem
The risk is not that AI reads records. The risk is that a team cannot later show who reviewed the output, which source record supported it, what changed, and who accepted the final position.
What gets reviewed
- Read audit trail from every extracted field to a source page using the source file and note the evidence path.
- Compare named-reviewer sign-off using the source file and note the evidence path.
- Locate electronic recordkeeping guidance such as FAA AC 120-78 using the source file and note the evidence path.
- Challenge EASA AMC material using the source file and note the evidence path.
- Summarize supporting release paperwork using the source file and note the evidence path.
Scope this review
Tell us the asset, the event, and the evidence in scope, and we will outline a focused first engagement.
Send a representative, redacted record set and we will scope the review.
What gets validated
- A source link must exist for audit trail from every extracted field to a source page; absence creates a finding.
- Reviewer notes must explain why an AI flag was closed.
- Conflicting dates, serials, or references stay open until the source hierarchy is clear.
- The regulatory acceptance scope must include the records that drive the current decision.
Evidence normally required
Common discrepancies
- Surveillance finding because a compliance status cannot be traced to a person and a document.
- Or model output treated as if it were itself a maintenance record.
- The issue appears only after the acceptance point.
What is at stake
Without governance, AI-assisted records review can create audit exposure even when the technical conclusion is right. The organization may lack the trail needed to defend a status, correction, or records decision.
How the work runs
Map intended use
Define where AI assists records review and which decisions remain controlled human actions.
Review governance controls
Check source retention, audit trail, reviewer identity, exception approval, and data handling.
Identify acceptance gaps
Flag places where records decisions lack accountable review or source trace.
Write control model
Deliver governance actions, workflow boundaries, and evidence-retention requirements.
What the buyer receives
- regulatory acceptance discrepancy register
- source-linked evidence map
- risk-ranked closure plan
- missing-record request list
Who uses the output
- CAMO manager use the register to decide which exceptions affect the event.
- quality and compliance manager use the evidence map to request or close source records.
- airlines leaders use the summary to brief the next approval, release, or deal meeting.
How the work fits into the transaction or program
This belongs before a regulated team adopts AI-assisted records review at scale. It does not seek regulatory acceptance of a tool by itself. It defines the controls needed for accountable source-record review inside existing airworthiness responsibilities.
Start with a single asset
Confirm the status list matches the underlying evidence.
Regulatory limits
The output is not a maintenance release, conformity statement, or regulatory approval. Responsible operators, owners, CAMOs, designees, and authorities keep those decisions.
What this review does not cover
- Regulatory approval activity
- Vendor procurement
- Legal drafting
- Physical conformity inspection
Specific to this review
- The control model starts with source retention and reviewer accountability.
- AI output is treated as assistance until a responsible person accepts or rejects it.
- Audit trails need source, extraction, review, disposition, and final-use records.
- Data-handling controls matter because aircraft records can include confidential transaction and operator material.
- The output helps quality, records, and accountable managers set boundaries before rollout.
Sources
Federal Aviation Administration. FAA acceptance criteria for electronic recordkeeping systems and electronic signatures.
European Union / EASA. Continuing airworthiness, maintenance records, CAMO responsibilities, and the airworthiness review process in the EASA system.
U.S. Government (eCFR). Maintenance recordkeeping content and approval-for-return-to-service requirements, including 43.9, 43.11, and Appendix B.
Frequently asked questions
Can AI be used in regulated records work?
It can assist, but the organization needs source trace, human accountability, and an audit trail for the final decision.
Does this approve a software tool?
No. It reviews workflow controls and evidence governance for AI-assisted records review.
Relevant glossary terms
Related pages
Where this fits
Talk to an engineer who has done this work
We will walk through your current state, the records or evidence involved, and a scoped first engagement.
Talk through the aircraft, records, evidence, deadline, and next useful step.