AI Governance for Life Sciences — a Practical Guide with Templates

The six moving parts

• Policy pack. Acceptable use, data handling, model risk, HITL, incident response. Short. Enforceable.
• Model registry. Every model, every version, every evaluation. A single source of truth, not a spreadsheet.
• Use-case intake with risk classification. No model deploys without a completed intake. High-risk cases hit the assurance stack; low-risk cases move fast.
• HITL patterns per risk class. Human-in-the-loop enforced at the tool, not in a slide deck. See Human-in-the-Loop reviews.
• Drift and incident response. Monitors with defined escalation paths and rollback plans.
• Red-team cadence. A scheduled adversarial test of every deployed model.

Who owns each part

• The AI CoE owns the operating model — the intake, the registry, the cadence, the reviews.
• The use-case sponsors own delivery to the operating model — a working use-case is one that passes intake, assurance and HITL design.
• The assurance function owns the review gates. AI validation services sits here.

How this maps to Annex 22

The published governance, the benchmark set, the safeguards, the HITL policy, the drift monitor and the fallback are what EU Annex 22 (AI in GMP) is asking for. Our Annex 22 blueprint goes deeper.

Where governance goes wrong

• The policy pack is written but the intake is optional. → Nothing changes.
• The registry exists but is not the single source of truth. → Shadow AI grows.
• HITL is a paragraph, not a tool control. → Enforcement fails at scale.
• Drift is monitored but has no escalation. → The tripwire is decorative.

Delivered as a service

Our AI governance team stands the operating model up, and our AI CoE team runs it with you until it's self-sustaining.