Risks of AI in Life Sciences

Every system that touches patient data or regulated processes must be traceable, justified, and auditable. The promise of AI introduces a central question of control. Losing control of AI systems can compromise sensitive research or patient data and disrupt traceability across validated processes, making accountability harder to define. 

Who owns the risk when models misbehave, data leaks, or vendors change their practices? The answer depends on whether you build AI or buy it. 

Buying AI: Trust, but Verify

Integrating commercial AI, such as copilots, SaaS tools, or embedded LLMs, means inheriting risk that cannot be fully governed. Most AI products are black boxes. You cannot verify how data is processed, logged, or reused. 

For regulated organisations, this lack of visibility can compromise data integrity when AI handles study results, quality data, or proprietary information that must remain within controlled systems. 

Cyberhaven Labs Q2 2025 report found that only 11% of AI tools qualify as low or very low risk. Over 83% of enterprise data reaching AI tools goes to medium or high-risk platforms, and nearly 35% of that data is considered sensitive. Cisco 2025 State of AI Security showed that only 27% of organisations have visibility into how AI tools use and share data, and fewer than 40% have AI-specific policies. 

This is just another supply chain issue. AI vendors act as processors of regulated or proprietary data and must be controlled with the same discipline as any critical dependency. 

Practical Steps for Safe Commercial AI Adoption

To manage commercial AI safely: 

• Restrict access to validated or sensitive data using DLP controls such as policy-based filtering or conditional access 
• Isolate AI tools through browser or network control layers such as CASBs or secure web gateways to monitor and restrict data flow 
• Define contractual and governance requirements through DPIAs, data use clauses, and retention restrictions before tool adoption 
• Classify AI vendors as high-risk suppliers and manage them within your QMS and supplier oversight framework 

You wouldn’t know where the user input ends up, models retrain, and vendor practices change without notice. When AI interacts with validated or regulated data, treat it as a controlled system subject to continuous monitoring and verification. 

Building AI: Control Comes with Consequences 

Developing AI internally provides transparency but shifts full accountability to the organisation. Training models on lab or clinical data, creating analytical copilots, or embedding AI into manufacturing introduces exposure to insecure pipelines, poisoned data, and model drift. 

Darktrace 2025 report shows attackers using prompt injection and poisoned data to manipulate internal models. Many models degrade silently without clear indicators of failure. 

AI behaves like a dynamic system, not a fixed configuration. Without regular oversight, internal models can drift from expected performance or rely on outdated data, leading to results that no longer reflect reality. GAMP Category 5 systems require full lifecycle control, and the GAMP 5 AI Guidance expands this with specific recommendations for AI risk management: 

• Start with a clear impact assessment 
• Treat AI as continuously learning and re-assess after major updates 
• Monitor input quality and model performance over time 
• Add explainability and human review for critical outcomes

ISO/IEC 42001 extends ISO 27001 principles into AI governance. It defines structure for policy, roles, and training that ensure AI is managed with the same rigor as information security. In biotech, adoption is still early but it fits the context well. 

When building AI, include it within your quality management system from the start. Every pipeline, dataset, and retraining event becomes part of the validation scope. Documentation, testing, and behavioural monitoring must confirm that the model operates consistently and safely. 

Where Biotech Differs 

Life Sciences share common governance and compliance frameworks, but biotech often operates closer to innovation, bridging research and regulated production, moving data and models faster than traditional validation cycles allow. This rapid pace makes it harder to maintain consistent validation evidence and trace data lineage across environments, increasing pressure on quality and compliance functions. 

This environment turns biotech into a testing ground for how AI governance evolves. Generative AI compounds the risk. Many AI services store, log, or reuse inputs, creating exposure for proprietary or sensitive material. Even anonymized text can reveal source data through context. 

Validation must show that AI systems behave securely, consistently, and with explainability across their lifecycle. GAMP 5 guidance emphasizes continuous verification, monitoring after updates, and evidence trails for every change. GAMP recommends practical guardrails: 

• Use interpretable models when possible 
• Log all inputs and outputs for auditability 
• Test edge behaviour with synthetic data 
• Apply structured review for AI-generated content 

If AI influences clinical eligibility, materials disposition, or quality decisions, evidence of control must be available before regulators ask for it. 

What to Do Now: Treat AI as Critical Infrastructure 

AI should be treated as critical infrastructure, governed and validated like any other regulated system. 

For commercial AI: 

• Segment tools from validated environments
• Classify and audit vendors under supply chain management
• Review contracts, data flow, and retention 
• Monitor outputs for drift and silent failure 

For internal AI: 

• Integrate AI controls into QMS and ISMS frameworks 
• Validate model behaviour, retraining, and change management 
• Apply GAMP 5 lifecycle principles: assess, control, monitor, improve 
• Adopt ISO/IEC 42001 for structured AI oversight 
• AI functions as infrastructure that requires governance, maintenance, and proof of reliability

How MIGx Can Help 

The rapid integration of AI into biotech brings efficiency, insight, and a new attack surface to deal with. Addressing the risks of AI requires a shift in mindset: from experimentation to governance, and from curiosity to control.  

MIGx works with Life Sciences companies to secure AI, from pilot to production: 

• AI supply chain assessments and vendor control plans 
• Secure internal AI platform design with data guardrails 
• GAMP and ISO-aligned validation planning 
• Security architecture and monitoring across AI services 

If your AI program needs structure, we can help you bring it in line with regulatory requirements.