Read a health-science librarian's framework for ethical AI use this week. It read like a clean engineering spec: clear scope, named data boundaries, provenance rules. Better than most enterprise AI policies I've had to build against.

The thing that stuck with me: governance is an architecture decision. You make it on day one, or you rewrite the feature later to bolt it on.

We build AI features for universities and libraries, so we live inside these constraints. Here's what we now decide first, in build order:

1. Log the call before the prompt. Every model request gets an audit record: input, output, model version, who triggered it. We add this on an empty feature. Retrofitting a trail across 40 endpoints is a week you won't get back.
2. Name what the model can't see. Before wiring anything, we list the fields it must never read. Student records, licensing terms, anything under an e-resource contract. That list becomes a hard boundary in code, not a review comment someone forgets.
3. Ground every answer, cite the source. No free-floating generation over library content. Retrieval first, then the model answers only from what it pulled, with the source ID attached. Librarians have done provenance for a century. We copied their homework.
4. Version prompts like code. Prompts live in the repo, reviewed in PRs, tagged per release. When an answer goes wrong in March, we can pull up the exact prompt that shipped in January. A prompt buried in a config table is an outage you can't debug.
5. Put the human where being wrong is expensive. Cheap, reversible actions run automatically. Anything that emails a faculty list or edits a catalog record waits for a click. We decide this per action, up front.
6. Store the why, not just the output. We keep the retrieved context and the reasoning trace next to the result. Six months later, "why did it say that" has an answer instead of a shrug.
7. Write the off switch first. Every AI feature ships with a flag that kills it without a deploy. We built that switch on our last project before the feature did anything useful. Used it twice in the first month.

None of this is exotic. It's boring, honestly, which is sort of the point. The teams that get burned are the ones who treat governance as paperwork for after the demo.

Curious what other builders keep in the day-one bucket that I've still got parked under "later."

A librarian's AI-ethics framework was a cleaner spec than most enterprise AI policies I've read. 7 governance calls we now make on day one