Track 03 · Regulated AI
How do regulated companies deploy AI without creating a compliance incident?
By treating the AI like any other regulated system: scope its access, gate risky actions behind human approval, log everything for audit, and hold it to the same rules the rest of the business follows. The compliance gap is not AI itself, it is AI deployed without the controls your industry already requires.
Healthcare, legal, financial services, and other regulated environments carry a higher bar. This track covers what changes when AI touches regulated data, and what it costs to get it wrong.
The reading path
Start here, then this.
- Lesson Audit trails in practice
- Template AI risk register
Common questions
Straight answers.
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Can we use commercial AI tools with regulated data at all?
Often yes, but only with the right agreements and controls: a signed BAA where PHI is involved, scoped access, audit logging, and a clear record of what was sent where. The tool is not the whole question, the deployment is.
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What does auditable AI actually require?
A reconstructable record: what the system saw, what it produced, who approved it, and when. If you cannot answer that six weeks later, you cannot defend the system to a regulator.
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What is the real cost of getting this wrong?
Beyond fines, it is the remediation, the loss of trust, and the projects frozen while you sort it out. The compliance gap is cheaper to close before deployment than after an incident.
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.