Using AI to Write Your Privacy Policies? Here's What to Watch Out For
AI is genuinely good at writing policies. I'll say that up front, because it's true, and because the rest of this article is going to be a bit of a cautionary tale, and I don't want you walking away thinking the answer is to abandon the tool entirely.
The answer is not to abandon it. The answer is to use it properly.
More and more of the teams I work with are turning to AI to generate their privacy and data protection policies. It makes sense. Policies are time-consuming to write, the subject matter can be dry and technical, and a good AI tool will produce something that looks polished and professional in about thirty seconds. For a founder or CTO juggling fifty other priorities, that's genuinely attractive.
But there are a few ways this goes wrong, and they're worth understanding before you hit publish and send the link to your clients or your regulator.
The legislation problem
AI tools are trained on data that has a cutoff point, and privacy law moves fast. I have seen tools confidently cite legislation that no longer reflects the current state of the law, or apply frameworks from one jurisdiction to another where they simply don't belong. If you're operating across Canada, the EU, and the US, the differences between PIPEDA, GDPR, and state-level privacy laws like CCPA matter enormously. A generic AI-generated policy is unlikely to navigate those nuances correctly without significant prompting and human review.
The hallucination problem
AI tools will sometimes add responsibilities, obligations, or processes that have nothing to do with what your business actually does. They're pattern-matching against every privacy policy they've ever seen, which means you can end up with a policy that references data retention schedules for categories of data you don't collect, or appointment of a Data Protection Officer when your size and processing activities don't require one. A policy that doesn't match your actual operations isn't just unhelpful. It's a liability.
The complexity problem
One of the most consistent things I see with AI-generated policies is that they are long. They pull in best practices from heavy enterprise frameworks and produce documents that would make even a seasoned privacy lawyer reach for a coffee. Plain English policies that your team can actually read and follow will do more for your compliance posture than a forty-page document that is never opened.
The principle I come back to, time and again, is that your policies need to match what you actually do. A policy that covers every conceivable eventuality but doesn't reflect your real operations is not a safety net. Regulators and clients are increasingly looking at whether your practices align with your stated commitments, not just whether the document exists.
So use AI, but treat it as a drafting assistant, not a compliance officer. Use it to build the structure, generate the initial language, and prompt you on sections you might have missed. Then have someone who knows the space review it against your actual data flows, your jurisdictions, and the specific legislative requirements that apply to your business.
That review step is the one that tends to get skipped. It's also the one that tends to matter most when something goes wrong.
If you want a second set of eyes on what your AI tools have produced, or you want to talk through how to prompt them more effectively for your specific situation, I'm available for that conversation. And if you think it would be useful for your team to hear this in person, I'm also available to speak on responsible AI use in compliance workflows.