Track 03 · Regulated AI
How do regulated companies get AI into production without turning it into an incident?
The expensive part of an incident is rarely the fine. It is the remediation, the projects frozen while you sort it out, and the trust you spend a year rebuilding. You avoid it by treating the system like any other regulated one: scope its access, gate risky actions behind a person, log what an auditor would ask for, and hold it to the rules you already follow.
Healthcare, legal, and financial services carry a higher bar, and clearing it late is paid for in stalled quarters and remediation hours rather than fines alone. This track covers what changes when AI touches regulated data, and what evidence you want already sitting on the shelf before anyone asks for it.
The reading path
Start here, then this.
- Lesson Risk tiers and approval gates
Decide which actions need a person and which can run unattended.
Common questions
Straight answers.
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Can we use commercial AI tools with regulated data at all?
Often yes, and the deciding factor is the deployment rather than the logo on the tool. A signed BAA where PHI is involved, scoped access, audit logging, and a clear record of what went where. Get those wrong and no vendor choice saves you.
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What does auditable AI actually require?
A record you can reconstruct without a fire drill: what the system saw, what it produced, who approved it, and when. Teams without it lose weeks of staff time answering one question. Teams with it answer in an afternoon.
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What is the real cost of getting this wrong?
Fines are rarely the biggest line. Remediation, the projects frozen while you sort it out, and the customers who now want written assurances cost more, and they land on people you cannot spare. Closing the gap before deployment is the cheap version of the same work.
Ready to act on this?
The reading path is the self-serve version. When you want it applied to your own business, this is the next step.
Book an intro call Make your AI audit-ready before it ships
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