AI Governance in the Aerospace Back Office: Where the Audit Trail Goes Missing
Aerospace and aviation companies are pulling AI into their engineering, quality, and compliance back office. The governance gap shows up in the records those teams produce, well before it would ever show up in the avionics they ship.
The rigor stops at the certified product
Aerospace companies have spent decades hardening the path from requirement to certified part. The FAA recognizes RTCA DO-178C as an acceptable means of compliance for airborne software through Advisory Circular AC 20-115D, which demands full requirements-to-code traceability, configuration management, and design assurance levels keyed to the severity of a failure condition. All of that rigor lands on the product. The engineering and compliance work behind it runs with far less, including the engineer who pastes a draft compliance matrix into a public model to speed up a tedious afternoon.
Inside the back office, AI adoption is moving faster than the controls around it. Take a quality engineer drafting an AS9100 audit response: the easiest move is to hand the first pass to a chatbot, clean it up, and submit it. The chatbot step never appears in the design assurance the certification authority eventually reviews, so the exposure accumulates without anyone tracking it.
Certification evidence assumes every step has a controlled author
DO-178C traceability works because every artifact has provenance. A requirement traces to a design element, to source, to a test, and someone owns each link. An AI tool that drafts a compliance matrix or proposes a Material Review Board disposition becomes a contributor the traceability scheme was never built to capture. If that draft moves into the certification package without a record of what model produced it, on what data, and who reviewed it, there is simply nothing to point to later. When an auditor samples MRB records months afterward and asks how a disposition was reached, your team has to reconstruct an answer that was never written down.
Supplier and parts traceability carries the same exposure. A buyer who uses AI to summarize a supplier's nonconformance history is making a quality judgment on a summary. Drop one detail in that summary and the disposition inherits the error. What the record shows is a clean human decision, with no trace of the machine step that shaped it.
During an audit, a quality action that has no record of how it was produced is exactly the kind of thing that turns into a corrective action request.
EASA has already defined what trustworthy AI oversight requires
Europe's regulator has been explicit about the shape of trustworthy AI in aviation. The EASA Artificial Intelligence Concept Paper, Issue 2 sets out learning assurance, AI explainability, and human-AI teaming with meaningful human oversight. The paper is framed around AI inside aviation systems, but the principles map cleanly onto the back office. An engineer who cannot explain why an AI tool reached a conclusion, and cannot show that a qualified person stayed in control of the decision, has produced work that falls short of the standard the same company would apply to an onboard system. Oversight only holds up when it is recorded. A reviewer who approves an AI-drafted safety case rationale needs the approval logged against that specific output, not against a general policy stating that humans review things.
ITAR turns a careless paste into an export
Aerospace runs on ITAR-controlled technical data, and a public model lives outside the company. When an employee pastes a controlled drawing or a propulsion test parameter into a consumer chatbot, that data crosses a boundary that no system logged, so the compliance team has no event to investigate and nothing to remediate. We built LogicNerve after watching this exact failure mode: sensitive material leaving through an everyday tool, with no way to reconstruct what moved or when.
Governing this does not mean banning AI from the engineering floor. The control that actually closes the gap is routing every request through an approved, department-owned agent, so controlled data never reaches an unapproved model and the action is logged whether or not anyone goes looking later. Training and qualification records, MRB dispositions, and certification evidence hold up under scrutiny once the AI step inside them is policy-controlled and audited by default. The auditor who pulls a disposition and asks who decided it, and on what basis, gets a log instead of a reconstruction.
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Sources
FAA Advisory Circular AC 20-115D: Airborne Software Development Assurance Using RTCA DO-178C