It’s tempting to treat AI like a shiny new toy—fun to play with, full of promise, and, for now, mostly someone else’s responsibility.
But here is the truth: AI governance is no longer a nice-to-have. It is a risk management imperative. And since your organization handles personal data, everyone using AI has a role to play.
So where do you start? With what you already know.
It’s a great start if you’re doing risk assessments for your vendors, your infrastructure, and your partners. Now expand that thinking to AI Systems. Ask the same questions:
- What is the AI system used for?
- What data is going into it, and how reliable are the outputs?
- What would the impact be if your internal data became accessible outside your organization, or was used to train other models?
- Are you feeding sensitive data, like personal identifiable information (PII), into the AI system? Are you allowed to?
And here’s a big one: If PII is submitted to your AI system, intentionally or unintentionally, can you remove it? If not, that’s a red flag.
Transparency and trust
If you want your audience and subscribers to share more data with you, make sure they trust you on how you will use it.
Review your privacy policies and notices frequently for new data uses, including AI systems or automated processing using AI. Are people allowed to opt out of it and can you honor those opt-outs? Increasingly, laws require that kind of transparency and choice, even when they don’t, your audience expects it.
Accuracy, transparency, explainability are all requirements, sometimes by law, sometimes by those choosing to share data with you
AI governance is a team sport
Here is the part that tends to get overlooked: this isn’t just a tech or legal problem. AI touches every corner of your organization.
AI is everywhere, from being embedded in your products to Sales, Marketing, Customer Service, and technical teams using it. Someone might be pasting customer data into ChatGPT as we speak. (Scary, right?)
That’s why collaboration is key. Governance cannot happen in silos. Everyone handles data; therefore, everyone needs to understand the risks.
Your AI governance starter checklist
It is not about perfection but progression. It’s about visibility and control. So, here is a starting point:
- Map your AI touchpoints: Where is AI being used internally and by vendors? What does it do, and who’s responsible? What data is involved and how to track its use?
- Evaluate risks: What happens if data is mishandled? Can you trace and fix it?
- Test your outputs: Are your AI models over-saturating certain segments—or missing others entirely?
- Prioritize explainability: Can you explain why a subscriber was placed into a certain audience segment?
- Incorporate privacy and security by design: AI should be governed at every step—not bolted on as an afterthought.
We’re sharing a full checklist to help you go deeper into this process.
