Insights · AI governance ·
What does AI governance for a regulated institution actually require?
AI governance for a regulated institution is the set of controls that lets an AI system survive a regulator, a board, and an engineering review at the same time. It means clear accountability, auditable documentation, testing and human oversight before deployment, and continuous monitoring after, designed in from the first line rather than bolted on.
The three rooms it has to survive
Most AI in a regulated setting fails not in the lab but in one of three rooms. The regulator asks whether the system is explainable, documented, and compliant with the rules that apply. The board asks whether it creates exposure they will have to answer for. The engineering review asks whether it actually works, holds under load, and degrades safely. Governance that only satisfies one of these fails the other two. The discipline is designing for all three from the start.
What it actually contains
- Accountability. A named owner for every AI-influenced decision, with a clear record of who signs off and on what evidence. The institution owns the outcome, not the model or the vendor.
- Auditable documentation. A trail a regulator can follow: what the system does, what data it uses, how it was tested, and where the limits are.
- Testing and human oversight. Adversarial testing before deployment and a human in the loop where the stakes justify it, so a wrong output is caught before it reaches a patient, a customer, or a filing.
- Continuous monitoring. Governance is not a one-time certificate. Models drift; regulations change. The system has to be watched, re-tested, and re-approved on a schedule.
How I approach it
I have built these frameworks for clinical decision-support platforms operating under active regulation, including HTI-1 and emerging state AI law, where the system had to satisfy its regulator and its board while adoption still rose. The method is not a compliance checklist bolted on at the end; it is designed into the architecture, then stress-tested through an adversarial review before it reaches a decision-maker. That is the difference between AI that clears governance and AI that collapses in front of it.