If an AI control changes how the product must behave, it belongs in the product definition before the architecture hardens.
Controls shape the system
Human review, traceability, data minimisation, model routing and fallback behaviour are not policy attachments. They determine interfaces, storage, permissions and operating workflows.
When controls arrive near launch, teams discover that the evidence was never captured or that the chosen product cannot support the required intervention. Remediation becomes redesign.
Define evidence with behaviour
Every material AI behaviour should have a corresponding control and evidence path.
- What the system may decide, recommend or draft
- Which data and models may be used for each sensitivity class
- Where human approval, escalation or override is mandatory
- What is logged, retained and available for review
- How performance, drift and incidents change system behaviour
Governance can accelerate delivery
Clear controls reduce late uncertainty. Product, risk, legal and engineering teams can evaluate the same working evidence and make decisions before dependency and sunk cost narrow the options.