Can You Use ChatGPT for EASA Compliance Work?

Consider asking ChatGPT how a Part-145 organisation should manage continuation training for certifying staff. An answer might combine requirements, AMC guidance and practical suggestions in one fluent paragraph. Can you tell which statement comes from which source?

A regulatory requirement, an Acceptable Means of Compliance and a practical suggestion do not have the same status. Your approved exposition adds the organisation’s procedures for meeting those requirements. Even when an answer includes references, check that they support the statements and that the source types are clear.

That is the whole problem in miniature. ChatGPT is useful for EASA compliance work. It is not a compliance source. Used with a routine for checking, it saves real time. Used without one, it can leave you with polished text whose basis you have not verified.

What ChatGPT does well

For a lot of compliance work, a general assistant is a genuine productivity gain:

• explaining a requirement in plain English before you read the source;
• drafting an internal email, a management briefing or a training note;
• turning rough audit notes into clearer wording;
• suggesting audit questions or a checklist structure;
• summarising meeting notes or a long procedure;
• comparing two versions of a text you paste in;
• listing possible compliance angles to check.

Instead of a blank page, you start from a draft. That is a good use of AI.

The trouble starts when the draft becomes the decision.

Why EASA work is different from normal office work

If an AI-drafted email is weak, you edit it. If a meeting summary misses a point, you fix it.

A compliance output has a different risk profile. It can shape audit preparation, procedure control, training content, maintenance governance, safety management, continuing airworthiness or contractor oversight. A compliance professional must be able to show the basis for the work in an audit, an authority meeting or an internal review.

So the answer cannot only be useful. It has to be traceable. A usable compliance output shows:

• the applicable requirement;
• the source type: Regulation, AMC or GM;
• the version or amendment basis;
• the scope of the answer;
• any assumption or uncertainty;
• the evidence behind the conclusion;
• what still needs human review.

A general chat answer rarely shows all of that unless you ask for it every time.

Where fluent answers go wrong

The main risk is not that ChatGPT is useless. It is that it is useful enough to feel reliable.

A confident answer can still use old wording, mix regulation with guidance, treat an interpretation as a hard requirement, or miss the specific Part, Subpart, AMC or GM that changes the conclusion.

You will often catch this inside your strongest area. Compliance work crosses boundaries, though. A safety manager asks about training organisation requirements. A Part-147 instructor asks about maintenance organisation obligations. Outside your strongest area, errors are much harder to spot.

Source type is where this bites hardest. A requirement in the regulation is not the same as AMC. AMC is not the same as GM. GM is not the same as an authority FAQ. Your approved exposition is not the rule itself. A procedure may be required by the regulation, shaped by AMC, explained by GM and implemented through the exposition, and the way you write a finding or brief management depends on keeping those apart. If you need a refresher on how the layers relate, the guide to reading EASA regulations walks through them.

A six-step routine before you rely on an answer

Treat any ChatGPT answer about an EASA requirement as a lead, then run it through the same six checks every time.

1. Find the provision in EASA's official text. Go to the regulation or Easy Access Rules on EASA's site and locate the paragraph the answer depends on. If you cannot find it, the answer has no basis yet.
2. Confirm the version in force. Check the amendment or consolidated version that applies on the date that matters to you, not the one the model may have been trained on. The article on version-aware regulation tools explains why this goes wrong so often.
3. Label each point: Regulation, AMC or GM. Go through the answer sentence by sentence and mark where each claim actually comes from. Anything you cannot place is interpretation or good practice, and should be written as such.
4. Check it against your approved exposition. Your MOE, CAME, OM or equivalent is what your organisation has committed to. If it says something different from the answer, that difference needs attention, not a quiet overwrite.
5. Record the source. Note the provision, the source type and the version you checked, next to the draft. Without that note, the next reader has to repeat your work.
6. Decide who reviews it. Name the person who will review the output before it becomes a finding, a procedure change or a management statement. For anything that will be approved, released or submitted, that is not you alone and never the tool.

This is slower than pasting the answer straight into a document. It is much faster than defending an unsourced statement in front of an auditor. A check of this kind, worked step by step on one AI answer about 145.A.40, is in how to check an AI answer against the regulation.

Safe to paste, never paste

What you type into a general AI tool leaves your organisation's control. A short rule of thumb:

Usually safe to paste:

• public regulation text, AMC and GM from EASA's site;
• your own generic notes and checklists with no company detail;
• anonymised wording you have stripped of names, registrations and record numbers;
• questions about how something works in general.

Never paste, unless your organisation's policy explicitly allows it:

• audit records, finding files and corrective action plans;
• occurrence reports, safety reports and investigation material;
• controlled manuals and company procedures;
• customer, supplier or aircraft-specific data;
• personal data about colleagues or anyone else.

The full data rules belong in your organisation's AI policy. The aviation AI policy article covers what that policy should define.

Where a general assistant stops

The routine above works. It also puts all the structure on you: finding the source, checking the version, labelling the layers, recording what you checked. Every time.

That is the gap between a general assistant and one built for aviation work. The generic vs aviation AI article goes through the differences, and the approved sources article covers why the source has to be part of the answer.

Avioverse is one way of closing it. Its assistant, Metis, answers from a built-in EASA regulation library, labels the source type of what it quotes, keeps quoted text separate from explanation, and shows whether the answer was checked against sources. It still prepares rather than decides: you review, and the accountable person approves. The comparison page sets it next to ChatGPT and Claude.

Whether you pay for ChatGPT yourself or use a company workspace also changes what you should put into it. The article on personal AI subscriptions at work covers that side.

The rule to keep

Use ChatGPT, Claude or Perplexity where they are strong: thinking, drafting, summarising, exploring an unfamiliar topic.

For EASA compliance work, keep one rule. The AI can prepare the work. You verify the basis.

Before an AI-assisted output goes anywhere official, you should be able to say which provision it rests on, whether that is Regulation, AMC or GM, which version you checked, how it fits your exposition, where you recorded it and who reviewed it. If you cannot, it is still a draft.