Course Recap: A Governance Checklist for Shipping AI Features
⏱ 10 min
Seven lessons in, the shape of this course should feel less like a compliance mandate
handed down from elsewhere and more like a set of habits that belong in ordinary
engineering review, alongside “does this handle errors” and “did you check permissions.”
This closing lesson pulls those habits into a single, practical checklist you can
actually run against a real feature before it ships, and restates, one final time, what
this course is and isn’t.
What you'll learn in this lesson
A consolidated, practical governance checklist
Pulled from all seven previous lessons into one list you can run against a real feature.
How the checklist maps back to specific lessons
So you can go deeper on any item that surfaces a real gap.
Where this checklist stops being enough
The point at which a real feature needs a real legal review, not just this list.
What "done" looks like for this course
A working mental model, not a finished compliance program.
Prerequisites
All seven previous lessons in this course. This lesson doesn’t introduce new material,
it consolidates what came before.
Step 1: The checklist
Run this against any AI feature before it ships, or against one already in production
that hasn’t had this kind of review yet.
Data flow is documented
What personal data, if any, reaches the feature, and where does it go (which AI provider, if any)? Lesson 2.
A lawful basis question has an actual owner
Someone in your organization can state which basis applies, or has been asked to. Not left undecided by default. Lesson 2.
The feature has a rough risk-tier read, and high-stakes cases are flagged
Anything touching employment, credit, or essential services has been specifically escalated, not self-classified as minimal risk. Lesson 3.
AI involvement is disclosed where a person would reasonably want to know
Chatbots, AI-generated or AI-assisted content, and AI-driven recommendations all have a visible, honest disclosure. Lesson 4.
Ability output_schema and prompt construction are both minimized
Narrow, deliberate returns from execute_callback, and redacted, selectively-built prompts, not full objects passed through. Lessons 5 (and Course 4).
The feature has been tested against a deliberately varied input set
Not just the three clean examples that happened to work first. Lesson 6.
A compliance-capable audit trail exists
Provider, purpose, and data category are captured, not just ability name and user ID, and the log itself is access-restricted and has a retention policy. Lesson 7.
A named person or team owns "is this actually compliant"
Not left to whichever developer shipped the feature to informally decide. Lesson 1.
Step 2: Using the checklist as a real review gate
The checklist is most useful treated as a genuine go/no-go gate in a review process, not
a retrospective nice-to-have filled in after launch. A practical way to operationalize
it:
Attach it to the pull request that ships a new AI feature
A short comment or template section, not a separate meeting.
Treat an unchecked item as a decision, not a blocker by default
Some items may genuinely not apply, document why, don't just skip silently.
Revisit the checklist when a feature's behavior changes materially
A recommendation feature that starts auto-applying its suggestions is a different review than the original version.
Keep completed checklists as part of your compliance documentation
A filled-out checklist per feature is exactly the kind of artifact Lesson 3 and Lesson 7 both point toward keeping.
Step 3: Where the checklist stops being enough
This checklist is a strong default and a genuinely useful gate. It is not a substitute
for legal review in every case, and it’s worth being explicit about when to route past
it entirely to a qualified professional rather than treating a clean checklist as the
final word.
Any feature touching employment, credit, insurance, or essential public services
Escalate specifically, regardless of how the checklist scores.
Any feature processing data for users primarily in a jurisdiction your organization hasn't operated in before
Different jurisdictions can mean different obligations entirely.
Any feature making an automated decision that meaningfully affects a person without human review
Automated blocking, banning, or denial of service, specifically.
Any point where you're genuinely unsure whether a legal requirement applies
Uncertainty itself is the signal to ask, not to guess and ship.
Not legal advice, one final time
Everything in this course, including this checklist, is general, practical guidance
written by and for developers. It reflects widely-documented principles behind data
protection and AI regulation, not a legal opinion about any specific feature, site, or
jurisdiction. A real compliance program for a real organization needs qualified legal
counsel involved, not just a course and a checklist. Use this course to have a better,
more informed conversation with that counsel, not to replace one.
Apply it: run the checklist against your most recently shipped AI feature
Pick the most recent AI feature your team shipped, and run the eight-item checklist
against it honestly. Wherever an item is unclear or unaddressed, that’s your concrete
next action, not a hypothetical one.
Recap
This course made one consistent argument across eight lessons: the technical decisions
you make when building an AI feature, what an ability returns, what a prompt contains,
whether a feature was tested on varied inputs, whether an action left a record, are also
governance decisions, whether or not anyone labels them that way. The checklist in this
lesson is a practical default for catching the common gaps early. It is not, and cannot
be, a replacement for real legal counsel on the questions that actually require one.
Treat this course as a way to show up to that conversation prepared, not as a way to
avoid having it.