Part 3: Governance Models for AI in Regulated Teams
Building scalable oversight, ownership, and risk alignment in dynamic AI environments
This is the third installment in our series on AI strategy in life sciences.
“Part 1: The Strategic Imperative: Why Life Sciences Organizations Need a Deliberate AI Strategy,” argued that AI is a strategic imperative.
“Part 2: Fit for Purpose: Aligning AI Strategy to Your Company,” explored how to tailor AI strategy to your organization’s structure, function, and risk exposure.
This article focuses on how to operationalize that strategy — through practical, risk-aligned governance.
Because strategy without governance is just intent.
And in regulated environments, intent isn’t enough.
1. Why AI Governance Matters Now
Governance isn’t a compliance checkbox — it’s the only way to ensure AI systems are deployed in a manner that’s safe, auditable, and aligned with business value.
From FDA’s GMLP to the EU AI Act, regulators now treat AI not just as software — but as a living system: one that evolves, adapts, and requires ongoing oversight.
Frameworks like GAMP 5 Second Edition, ICH Q9(R1), and ISO/IEC 42001 (the new AI management system standard) make it clear: AI governance must be lifecycle-based, risk-tiered, and capable of dealing with non-deterministic behavior.
And yet, the biggest driver of governance isn’t regulation — it’s reality.
Across the industry, organizations of all sizes are under pressure:
Layoffs and hiring freezes have reduced headcount
Teams are wearing multiple hats across functions and systems
Investment is cautious, and ROI must be defensible
Everyone is being asked to “do more with less” — while adopting more complex systems than ever
Whether you’re a 50-person GxP software company or a multinational pharma division, constrained execution is the baseline.
2. What We Mean by “AI Governance”
When we talk about AI governance, we’re referring to the people, processes, and controls that ensure AI is:
Developed and deployed responsibly
Aligned with business and regulatory expectations
Maintained in a controlled, auditable, and risk-aware way
If your AI strategy defines what you plan to do — governance defines how you’ll do it safely and repeatably.
Good AI governance doesn’t require building something from scratch. It should extend and adapt your existing QMS, ISMS, validation, and change control processes.
AI systems bring new risks — especially post-deployment — but those risks can be managed if governance is clear, lean, and matched to how your organization actually works.
Governance doesn’t need to be heavy. But it must be clear.
3. Ownership: Who’s Accountable — Even at the Early Stage?
Most teams aren’t starting with custom-built models. Early AI adoption usually involves embedded features, vendor-supplied algorithms, or lightly configured tools. But even at this stage, AI outputs can impact regulated processes — which means they carry risk.
Governance should start early. You need to know:
Who notices when system behavior changes?
Who decides if action is needed — retraining, revalidation, rollback?
Who ensures decisions are documented and defensible?
We recommend defining ownership across three functional roles — tied to risk, not headcount:
System Owner - Oversees how the AI system or feature is used and monitored. Flags issues, makes operational decisions, initiates remediation.
Compliance Lead - Evaluates whether behavior changes impact validation scope, intended use, or regulatory posture.
Data/InfoSec Lead - Manages data integrity, system exposure, and rollback readiness. Ensures inputs, outputs, and logs are secure and traceable.
These roles don’t need to be separate people — especially in lean teams. They just need to be named, understood, and reviewed.
In practice, these decisions often route through an executive oversight committee — described further below — which brings together cross-functional ownership in a lean, centralized structure.
When the System Changes
Whether it’s a vendor update, new dataset, or shift in use, any change to an AI-enabled system deserves scrutiny. The key questions:
Is this within the intended use and defined risk tolerance?
Does it affect validation status or audit exposure?
Do we have a rollback path if something breaks?
Not every change warrants heavy overhead — but every change needs a conscious, documented decision.
Good governance is about clarity, not complexity. It keeps risk in check, even from day one.
🧪 Real-World Snapshot: Lean Governance at a GxP Software Vendor
At a previous engagement with a GxP software manufacturer, we helped implement a governance model for AI features embedded in their platform.
They didn’t have a formal AI team or the ability to spin up dedicated committees — but they still needed to manage compliance risk, validation scope, and post-deployment behavior.
We created a single executive oversight committee that:
Reviewed and approved AI-related changes
Owned monitoring thresholds and retraining triggers
Delegated technical work to engineering and QA
Retained centralized documentation and accountability
This structure helped the organization meet ISO/IEC 27001 and GAMP 5 expectations — without needing to stand up new governance infrastructure or overextend teams.
It worked because everyone knew:
Where decisions were made
How risk was reviewed
And who was responsible for escalation
🎯 The takeaway: You don’t need five layers of oversight. You need one place where the right conversations happen and the right decisions get made.
4. Reconciling AI with Traditional Quality Systems
Most QMS and validation frameworks assume static software. AI doesn’t fit that mold.
Models retrain. Outputs change. Behavior may shift over time, even without explicit code changes.
To stay compliant without overbuilding, focus on:
Snapshot Governance
Capture the state of a model at deployment, retraining, and decommission — just like you would a system baseline.
Drift Thresholds
Define measurable criteria that trigger review or pause (e.g., precision, false positives, or confidence intervals exceeding defined tolerances).
Retraining Controls
Include retraining logic in your change control process. Document not just what changed — but why retraining was necessary.
Audit Logs
Maintain a complete, centralized record of model performance, updates, and decisions — not just static validation documents.
This approach aligns with ICH Q9(R1) and the EU AI Act expectations for continuous monitoring — while staying compatible with existing QMS and CSV processes.
5. Calibrating Risk Tolerance Across Functions
AI governance doesn’t just live in QA. It spans R&D, IT, compliance, legal, and operations — and each group views risk differently.
R&D may tolerate variability for innovation
QA and Regulatory need traceability and repeatability
Legal cares about explainability, accountability, and vendor risk
IT and InfoSec care about infrastructure exposure and system boundaries
Governance helps align these perspectives by:
Defining acceptable risk levels based on use case category
Mapping who has decision rights for AI deployment, retraining, and rollback
Establishing a shared framework for risk-based oversight — regardless of department
It’s not about who’s involved — it’s about how clearly roles and responsibilities are defined and enforced.
6. Avoiding Governance Theater
Some companies implement AI governance in name only. They create committees that never challenge decisions, write SOPs no one follows, or validate a model once and never revisit it.
This doesn’t work — and in a regulatory environment, it creates both compliance risk and operational fragility.
Signs of real governance:
Actionable criteria for when a model is paused or retrained
Centralized logs of overrides and review decisions
Use cases retired or reevaluated based on performance or risk
Escalation pathways used — not just documented
The point isn’t to create overhead. It’s to build confidence, continuity, and audit readiness — without slowing delivery.
7. Composite Governance: A Model Built for Constraint
Some large organizations maintain separate governance boards for AI, quality, and information security. That structure works — if you have the organizational hierarchy to manage it and resources to staff and sustain it.
But most teams don’t. Not in the current environment.
Instead, life sciences companies — regardless of size — are adopting composite governance: a lean oversight model that integrates key disciplines into a single decision-making body. It centralizes visibility, reduces redundancy, and ensures issues are surfaced and resolved without delay.
This structure typically spans:
AI/ML systems and vendor tools
Quality and validation (GxP)
Information security (ISO 27001, access, auditability)
Regulatory expectations (FDA, EMA, EU AI Act, GAMP 5)
Composite governance isn’t a workaround — it’s a strategic response to constrained execution.
While it places significant responsibility on one committee, combining key disciplines enables pragmatic decision-making, coordinated action, and a lower risk of fragmented oversight. It streamlines operations without sacrificing control.
It works best when:
Roles and authority are clearly defined
Documentation supports risk-based decisions
The team stays focused on high-impact issues
Local systems have support for routine decisions and escalations
Hybrid Oversight Models
When organizational scale allows, some responsibilities can be delegated to sub-oversight groups or embedded system owners. This hybrid model helps reduce the burden on the central team — but it must be designed carefully.
Without structure, sub-oversight can become unwieldy, siloed, or misaligned.
To be effective, hybrid governance should be:
Risk-based — Delegation is based on impact and complexity, not job title or function
Bounded — Scope of authority is clear: what can be approved locally, and what must escalate
Auditable — Decisions are documented with rationale and traceability
Connected — Feedback loops are in place to review outcomes and refine processes over time
Governance isn’t just about making decisions — it’s about ensuring those decisions reduce risk, deliver value, and stand up to scrutiny.
Used well, this model lets the composite committee focus on strategic alignment and material change, while day-to-day oversight happens closer to where the work is done — without sacrificing traceability or accountability.
8. Conclusion: Governance as a Growth Enabler
Good governance doesn’t slow teams down.
It prevents rework, reduces audit risk, and enables scale by ensuring the right decisions are made — and that those decisions can be defended.
As AI adoption grows and regulatory expectations evolve, governance will separate the teams that can scale from the ones that stall.
This article assumes the reality most life sciences teams face:
You’re resource-constrained
You’re balancing innovation with compliance
You need to show value early — and maintain control over time
That’s why this model is built on clarity, flexibility, and lean ownership.
It scales with risk — not with company size.
You don’t need more process.
You need the right people, asking the right questions, with the authority to act — and a plan to pivot if things go sideways.
Governance isn’t just a regulatory obligation — it’s enlightened self-interest.
It gives teams the clarity, alignment, and risk control they need to move faster and scale responsibly — on the way to meeting their regulatory and compliance responsibilities.
📬 Need help designing a governance model that fits your organization, not someone else’s template?
Let’s build something that works — and holds up under inspection.
Contact Driftpin Consulting for an AI governance readiness review or composite model working session.
🔜 Coming soon: Part 4 – Continuous Validation for AI: How to Maintain Control as Models Drift, Retrain, and Evolve