In regulated and data-sensitive environments, the question isn't just “does the AI work?” — it's “can we deploy it without creating risk?” These are the questions worth answering before anything goes live.
Data
- Where does our data go? Which provider processes it, in which region, and is it used for anything beyond answering our request?
- Is it retained or trained on? Confirm in writing that your data isn't retained or used to train third-party models unless you opt in.
- What's the minimum data needed? Send the least the task requires. Mask or redact identifiers the model doesn't need to see.
Access & actions
- What can the system actually do? Reading data is low-risk; taking actions (sending, paying, deleting) needs tighter controls and, often, human approval.
- Who can use it, and is that logged? Role-based access and an audit trail of inputs, outputs, and actions are non-negotiable in regulated settings.
- How do we revoke it fast? There should be a clear kill switch if something behaves unexpectedly.
Reliability & oversight
- What happens when it's wrong? Define the fallback, the human-in-the-loop for high-stakes cases, and how errors are caught.
- Can we explain a decision? For anything affecting customers or compliance, you need to show what the system saw and why it responded as it did.
- Is it monitored? Accuracy and behaviour drift over time; you need alerts, not surprises.
Governance
- Which regulations apply? Map the workflow to your obligations (data protection, sector rules) before launch, not after.
- Who owns it? A named owner for the system, its risks, and its reviews.
Security designed in from the start is cheap. Security bolted on after an incident is not.
None of this needs to slow you down. Answer these questions up front, build the controls into the pilot, and you can move fast and safely — which is exactly what regulated teams need.