AI/ML Software Governance for Regulated Organizations

AI/ML components behave differently from traditional software: their outputs can shift as models are retrained, as underlying data changes, or as usage patterns drift from what was originally evaluated — often without any code deployment at all.

Governance is broader than change control

Change control asks: what changed, and was it evaluated before release? Governance asks a wider set of ongoing questions: who owns this component, what is it actually allowed to influence, how is its real-world performance monitored, and what happens when it drifts.

Core elements of an AI/ML governance framework

• A clear owner accountable for the component’s intended use and its boundaries.
• A defined scope of what decisions or records the component is permitted to influence.
• Ongoing monitoring for performance drift, not just a one-time validation at go-live.
• A documented process for what happens when monitoring detects unexpected behavior.
• Human oversight appropriate to how much the component’s output actually matters to a regulated decision.

Matching oversight to impact

Not every AI/ML use case needs the same level of governance. A low-impact drafting or summarization tool needs lighter oversight than a component whose output could directly affect a quality decision, a regulated record, or patient safety. The framework should scale with what the component actually touches — the same risk-based principle behind CSA.

Questions organizations should ask

• Who is accountable for this AI/ML component, by name, not just by department?
• What is it explicitly NOT allowed to influence?
• How would we detect if its behavior started drifting from what we evaluated?
• What is the actual process if drift or an unexpected output is detected?
• Is oversight proportionate to what this component can actually affect?