Course 16 covered what AI governance and GDPR-consistent data handling requires of a developer shipping a feature, at the level of “does this feature send personal data to a third party, and on what basis.” Delivering that same feature to a client adds a second, contractual layer on top of the technical one: your client is trusting you with their site, their customers’ data, and a decision about where some of that data now travels (to an AI provider, through your infrastructure, or both). Onboarding a client well means getting the technical access you need and getting explicit, written agreement on how their data is handled, before delivery starts, not after a question comes up.
Course 16’s data privacy and consent lesson, and Course 4’s audit-logging pattern. This lesson applies both to a client-facing agreement rather than your own product’s internal practices.
This lesson describes what a client data-handling agreement commonly addresses in practice, at a level useful for scoping an engagement and having an informed conversation with a client. It is not legal advice, it does not tell you what your specific contract should say, and it cannot account for your client’s jurisdiction, their own regulatory obligations, or the specifics of their data. A real client contract, and any specific commitments about GDPR or other regulatory compliance, should be reviewed by a qualified lawyer, ideally one familiar with your client’s jurisdiction, before it’s signed.
Step 1: A practical onboarding sequence
Step 2: What a data-handling agreement should name explicitly
This list mirrors, at a contractual level, the same technical questions Course 16 asked about a single AI feature: what data is sent, to whom, and on what basis. The difference here is that a client relationship needs the answer written down and agreed to in advance, not just reasoned through internally as you build.
Step 3: Audit and transparency as a commitment, not just a feature
Course 4 covered building a custom audit log for agent actions, since the MCP Adapter doesn’t ship one. In a client relationship, that pattern becomes something you can offer as an explicit commitment: the ability to tell a client exactly which AI-driven action happened, when, initiated by whom, if a question ever comes up about what their assistant did to their site or their customers’ data. A client who can be told “here is the audit trail for that action” is in a meaningfully stronger position than one who has to take your word for it, and offering that transparency proactively is a real differentiator in a client pitch, not just a defensive measure.
Step 4: Access scoping as part of onboarding, not an afterthought
The onboarding sequence in Step 1 deliberately puts access scoping after the data- handling agreement is drafted, not before. Once you know what data a feature actually touches, you know what WordPress capabilities and what abilities actually need exposing, which is the same least-privilege principle this track’s security lessons apply to abilities generally, just applied here to the credentials you personally hold for a client’s site. Requesting a full administrator account by default, because it’s easier than scoping a role, is the single most common way an agency ends up with far more access, and far more liability, than a given engagement actually requires.
Telling a prospective client “we only request access to what this specific engagement needs, not full administrator access” is a genuine trust signal in a sales conversation, not just good security practice. Clients increasingly ask this question themselves, having a real, specific answer ready is worth more than it costs to set up.
Recap
Onboarding a new AI/MCP client well means sequencing discovery, a written data-handling agreement, and scoped access before any delivery work begins, not treating the contract as paperwork to get through after the interesting work already started. A data-handling agreement should name the AI provider, the categories of data involved, retention, subprocessor status, and an offboarding plan, explicitly, not left as a vague assurance. Course 4’s audit-logging pattern becomes a real transparency commitment you can offer a client, not just an internal debugging tool. None of this replaces real legal review, the not-legal-advice caveat above applies to every part of this lesson, but having these questions already answered makes that legal review faster and cheaper than starting from a blank page.
Resources & further reading
- Data Privacy and Consent for AI Features, Course 16
- Building Your Own Audit Log for Agent Actions, Course 4
- AI Governance, Compliance & Responsible AI for WordPress, Course 16, the full governance framing this lesson applies to a client contract