Why regulated buyers have a harder problem
In financial services, healthcare, insurance, and the public sector, an AI mistake is not only an embarrassment. It is a reportable event, a breach of a supervisory obligation, or a privacy violation with statutory penalties. That raises the bar for any governance platform. Documenting that a risk exists is not enough when the regulator expects the risk to be controlled. The platform has to prove that sensitive data never reached the model, that only authorized users invoked it, and that every interaction is on the record. For these buyers, governance is an operational control, not a slide in a risk committee deck.
Enforcement at runtime versus documentation after the fact
Many tools marketed as AI governance are inventories and risk registers. They catalog models, capture assessments, and generate reports. That is useful for oversight, but it governs nothing at the moment the model runs. A regulated industry needs the control to operate where the risk lives: at the inference call. That means intercepting each prompt, redacting regulated data before it leaves the boundary, enforcing access and usage policy, and blocking requests that break the rules. The test to apply is direct. Can the platform stop a non-compliant AI action while it is happening, or can it only tell you afterward that it occurred?
The capabilities to require
Runtime interception and policy enforcement, so controls act on live traffic rather than reporting on it. Data redaction at the boundary, so customer, patient, and personal data is stripped before any prompt reaches an external model. Granular access control, mapping which roles and applications may use which capabilities. A complete, tamper-evident audit trail, because supervisory exams and incident response both depend on an accurate record. Fail-closed behavior, so a policy gap denies the request instead of leaking it. And mapping to the frameworks you answer to, such as the EU AI Act, ISO 42001, and sector rules, so the same control produces the evidence your auditors expect.
How to evaluate vendors
Run a real use case, not a checklist demo. Send a prompt containing regulated data and confirm the platform redacts it before it leaves your environment. Attempt an action your policy forbids and confirm it is blocked, not merely logged. Pull the audit record and check it captures who, what, and when in a form you could hand to an examiner. Ask how a new application inherits governance, because coverage that depends on each team wiring up controls will leave gaps. The platform that ships a governed use case into production, under enforcement you can evidence, is the one worth buying.
Frequently asked questions
What makes AI governance different in regulated industries?
The consequences are statutory. An ungoverned AI action can be a reportable breach or supervisory failure, so the platform must enforce controls at runtime and produce auditable evidence, not just document that a risk exists.
What should regulated buyers require from an AI governance platform?
Runtime interception and enforcement, data redaction at the boundary, granular access control, a tamper-evident audit trail, fail-closed behavior, and mapping to frameworks like the EU AI Act and ISO 42001.
Is a risk register enough for compliance?
No. A register documents risk but does not control it at the moment the model runs. Regulators increasingly expect the risk to be enforced and evidenced, which requires a runtime control layer.