AI Governance Framework for Life Sciences
Policies, model registry, use-case intake, risk classification, red-teaming and drift — designed for regulated environments.
AI governance in life sciences is the operating model that decides which AI use cases are allowed, who approves them, what evidence each one needs before production, and how they are monitored afterwards. It sits above individual system validation: validation proves one system is fit for use, governance decides which systems should exist and keeps an inventory of them.
- Signer’s name
- Date and time
- Meaning of the signature
- Entry in the audit trail
Policy and accountability
- 1
AI acceptable-use policy
What staff may and may not do with AI tools, including public consumer tools and company data.
- 2
Named accountable owner
One person accountable for AI governance, not a committee with shared responsibility and no decision rights.
- 3
Cross-functional review body
Quality, IT, regulatory and the business function. Without QA at the table, nothing reaches production.
- 4
Decision rights documented
Who approves a use case, who can stop one, and what gets escalated.
- 5
Regulatory scope mapped
Which of GxP, EU AI Act, data protection and local rules apply to each use case — they overlap but are not the same.
Use-case intake and triage
Most AI governance failures are intake failures: things reach production without anyone having classified them.
- 6
Single intake route
Every AI use case enters through one process. Shadow AI exists wherever intake is slower than a credit card.
- 7
GxP determination
Does this use case touch a regulated record or decision? Decided at intake, not after build.
- 8
Risk classification
A small number of tiers with clear criteria, each tier carrying a defined evidence requirement.
- 9
Build-or-buy and data residency
Where the data goes, who processes it, and whether it may be used for vendor model training.
- 10
Rejection is a real outcome
A governance process that has never declined a use case is a logging exercise, not a control.
Model registry
The inventory question — 'how many AI systems are in use and what do they touch' — is the one most organisations cannot currently answer.
- 11
Complete inventory
Every AI system in use, including features embedded in purchased software you did not think of as AI.
- 12
Version and provider recorded
Model version, vendor, deployment region, and the contractual position on model changes.
- 13
Intended use and criticality
Linked from the registry so the evidence requirement for each entry is visible.
- 14
Validation status and evidence location
Where the validation package lives for anything GxP-relevant.
- 15
Review date
When each entry is next due for reassessment, with an owner.
Pre-production assurance
- 16
Evidence requirement matched to tier
High-criticality use cases get full validation; low-risk internal tools get proportionate checks, not the same package.
- 17
Red-teaming for consequential use cases
Deliberate attempts to make the system produce a harmful or wrong output, with findings recorded.
- 18
Human oversight design reviewed
Someone checks that the reviewer can realistically do the reviewing the design assumes.
- 19
Go-live approval recorded
A dated decision by a named approver, not an absence of objection.
In production
- 20
Monitoring proportionate to criticality
Drift, override rates and incidents, reviewed by someone with authority to suspend the system.
- 21
Incident process includes AI failures
A wrong model output is an incident, routed like any other quality event.
- 22
Periodic review
Is the intended use still accurate, is performance holding, is the fallback still workable.
- 23
Decommissioning handled
Retired AI systems leave the registry with a record, and their outputs remain traceable for the retention period.
Governance and validation are different jobs. Validation proves one system is fit for its intended use; governance decides which systems should exist, keeps the inventory, and makes sure nothing reaches production unassessed.
The fastest way to find out whether your governance works is to try to answer: how many AI systems are in use here, and which of them touch a GxP record? If that takes more than an afternoon, the registry is the place to start.
This is a working aid, not a regulatory document, and it does not replace your own quality system procedures.
Explore related resources, products and services: GxP Copilot, TraceDraft, validation services, AI transformation.
Sources
Primary documents referred to on this page, from their official publishers.
Common questions
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What is AI governance in a regulated life sciences company?
It is the operating model that decides which AI use cases are permitted, who approves them, what evidence each needs before production, and how they are monitored afterwards. It maintains an inventory of AI systems in use and their regulatory status, sitting above the validation of any individual system.
How is AI governance different from AI validation?
Validation proves one system is fit for a defined intended use. Governance decides which systems should exist at all, classifies their risk, sets the evidence bar for each tier, keeps the registry, and monitors the portfolio. You can validate a system beautifully and still have an uncontrolled AI estate.
What should an AI model registry contain?
Every AI system in use, including AI features inside purchased software; the model version, vendor and deployment region; the contractual position on model changes; the intended use and criticality classification; the validation status and where the evidence lives; the accountable owner; and the next review date.
Does the EU AI Act apply to pharmaceutical companies?
It can, depending on the use case. The AI Act classifies by risk and application rather than by industry, so an AI system used in recruitment or in a medical device may fall under it while an internal drafting tool may not. It applies alongside GMP obligations such as Annex 22, not instead of them.
How do we stop shadow AI use?
Make the approved route faster than the unapproved one. Shadow AI appears where intake is slower than a corporate credit card. A clear acceptable-use policy, a low-friction intake process, a small set of sanctioned tools, and a genuinely quick turnaround for low-risk use cases do more than prohibition.
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