AI design control: what practitioners need to govern drift

TL;DR: AI approval is only a moment unless teams keep the live system aligned with the approved design, according to JetStream Security research, because models, tools, data sources, and identities can change without a new sign-off. The control problem is not documentation alone but proving that the running system still matches what auditors and standards expect.

Editorial analysis by NHI Mgmt Group, based on content published by JetStream Security: “A Policy Is Not a Control”.

Key questions

Q: What breaks when AI approval is only a one-time review?

A: Approval without ongoing design control leaves no reliable way to prove the live system still matches what was approved.

Q: Why does AI governance need both oversight and evidence capture?

A: Oversight without evidence is hard to defend, and evidence without oversight is just recordkeeping.

Q: How should teams govern model, tool, and identity changes together?

A: Treat them as one change-managed system.

Practitioner guidance

• Implement a design of record for every AI system Name the models, tools, data sources, and identities that make up each production AI workflow, then keep that record aligned with runtime reality.
• Version every material AI change Require a new reviewable version whenever the system changes in a way that could affect behavior, access, or compliance evidence.
• Tie approvals to runtime evidence Make sign-off contingent on being able to show the current system still matches the approved design, not just that the approval happened.

Bottom line: AI approval becomes weak control if the live system can diverge from the documented design without a new review.

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