This course is not legal advice. It’s written by and for WordPress developers, and it explains general, widely-documented principles behind data protection and AI regulation so you can make better-informed technical decisions. It is not a substitute for advice from a qualified lawyer, and no specific compliance decision for a real site should be made on the basis of this course alone. Every organization’s legal exposure depends on facts this course cannot know, what data you actually collect, which jurisdictions your users are in, and what your business does with that data. Treat everything here as a starting point for a conversation with your own legal counsel or your client’s, not as a finished answer.
With that said, here’s why this course exists at all. Every previous course in this track
treated “should this ability expose this field” or “should this AI feature run
automatically” as an engineering question. It’s also, unavoidably, a compliance question,
and in most organizations the developer is the only person in the room who actually knows
what data an AI feature touches, where it goes, and what a user would see if they asked.
Legal and privacy teams can write policy. They cannot see inside execute_callback. That
gap is where this course lives.
This is an intermediate-to-advanced course about the practical side of AI governance for WordPress: what data privacy and consent mean for a feature that sends content to a third-party AI provider, what the EU AI Act’s risk-based structure means for a typical WordPress AI feature (almost none of them are high-risk, and this course explains why, generally, and why you should still check), how to disclose AI-generated content honestly, how to keep personal data out of prompts in the first place, how to think about bias and fairness in AI-driven features like moderation or recommendations, and how to extend the audit-logging pattern from earlier in the track into something a compliance review could actually use.
This course assumes you’ve completed
WordPress AI Foundations (Course 1) and
WordPress MCP Security & Authentication
(Course 4). Course 4 built the technical security foundation, permission checks, data
minimization in output_schema, and a custom audit log. This course builds the
governance and compliance layer on top of that foundation, and reuses both patterns
directly rather than inventing new ones. If you haven’t built a working ability with a
permission callback and haven’t seen the custom audit-log pattern from Course 4, start
there first, this course leans on both throughout.
By the end, you’ll have a practically and ethically defensible approach to shipping AI features: one that treats data minimization, disclosure, and fairness as real engineering requirements, keeps an honest record of what a compliance reviewer would actually need, and knows, clearly, where “developer judgment” ends and “ask a lawyer” begins.