Validated Today, Broken Tomorrow

Validated Today, Broken Tomorrow:

Why Public AI Is a Hidden Validation Risk in MedTech Manufacturing

Introduction: When Your "Validated" AI Quietly Changes Overnight

Public AI systems like ChatGPT, Copilot, and Gemini are becoming incredibly powerful and increasingly tempting for MedTech companies trying to accelerate documentation, risk analysis, or verification activities.

But beneath the convenience lies a structural problem no one in manufacturing can afford to ignore:
Public AI systems change at any time, without notice, even when the version number stays the same.

For regulated industries that rely on frozen baselines, traceability, and years of process stability, this creates a difficult question:

How do you validate a tool that continuously rewrites itself?

In my earlier article titled Non-Deterministic AI and Hallucination: What Manufacturing Leaders Must Understand Before Trusting the Machine, I explored why AI outputs vary from one run to the next and how hallucinations undermine trust.

This article goes one step further.
Even if you reduce hallucinations, public AI is still a moving, opaque black box that can silently invalidate your process validation at any moment.

1. The Core Problem: Validation vs. a Moving Target

Traditional MedTech validation relies on one key assumption:

The tool I validate today will behave the same tomorrow.

Public AI breaks this assumption completely.

Model providers change:

• The underlying weights

• Safety filters

• System prompts

• Retrieval tools

• Training data sources

Often these changes occur without any detailed change log and without keeping backward-compatible behavior.

This means your validated AI is only validated for the moment you performed the validation.

When the AI changes, the validation evidence may no longer be valid.

In a manufacturing environment where devices stay in production for 10 to 30 years, this creates a significant lifecycle risk.

2. Why Public AI Fails GxP Validation Expectations

A. Invisible model changes result in uncontrolled process changes.

MedTech relies on controlled and documented changes.
Public AI relies on continuous and undocumented improvement.

These two worlds are fundamentally incompatible unless you have local control.

B. Version numbers do not represent functional equivalence.

AI vendors routinely update models while keeping the same top-level version identifier.
Your validation package might reference Model X, but Model X today is not the same as Model X tomorrow.

This is similar to:

• An automated inspection system changing its logic overnight.

• A CMM using a different metrology kernel without an ECO.

• A PLC rewrites its own logic during production.

And no one informs you.

C. Long product lifecycles do not align with short AI lifecycles.

Public AI models are typically:

• Updated often

• Released annually or quarterly

• Supported only for short periods

• Replaced quickly based on commercial priorities.

Medical devices, however, require:

• Stable validated processes

• Long-term reproducibility

• Traceability that lasts decades

• Consistency during post-market investigations

Using a public AI model in verification or documentation could force teams into repeated re-validation cycles, sometimes with no warning at all.

3. Additional Risks Companies Often Overlook

A. Non-determinism and hallucination

Even within a single model version:

• The same prompt may yield different outputs.

• AI may invent information with high confidence.

This creates unacceptable variability in:

• Work instructions

• Inspection criteria

• Risk documentation

• CAPA investigations

• Labeling and IFU content

As I have said before:
If you would not let an intern rewrite your work instructions unsupervised, you should not let an unvalidated AI do it either.

B. Audit trail gaps and poor reproducibility

Public AI systems typically do not offer:

• A version identifier tied to each response.

• Full prompt and output logs

• Detailed training data lineage

• Guaranteed reproducibility.

If an auditor asks how, you know a document was created under a validated AI configuration, you may not have an answer.

C. IP leakage and data residency concerns

Public AI runs outside your controlled environment.
Uploading CAD files, risk analyses, DHF or DMR content, or complaint narratives can expose sensitive information to external processors.

Even if a provider claims not to train on your data, you still must trust:

• Their cybersecurity

• Their access controls

• Their internal subcontractors

• Their international data routing practices

For MedTech, this is a regulatory risk, an IP risk, and a patient safety risk.

D. Shadow AI and governance gaps

Employees often use public AI tools informally to:

• Simplify documents.

• Clarifying requirements

• Write procedures.

• Summarize data.

• Draft analysis

This leads to:

• Uncontrolled changes

• Unapproved content creation

• Missing traceability

• Supplier qualification gaps

Without strong governance, public AI becomes a new source of uncontrolled process drift.

4. The Long-Tail Problem: MedTech Requires Tools That Remain Stable for Decades

This is the central challenge for MedTech operations.

A public AI system:

• Changes often

• May be deprecated at any time.

• May be replaced without backward compatibility.

• Cannot be locked to a validated configuration.

In contrast, your manufacturing process:

• Must remain controlled for decades.

• Must be reproducible for audits.

• Must keep consistent performance.

• Must comply with QMSR, ISO 13485, and ISO 14971

• Must support investigations long after production.

Public AI cannot fulfill these long-tail requirements.

5. The Solution: Local LLMs You Control Instead of AI You Rent

NoioMed uses a fundamentally different model:

Local LLMs hosted in validated private VPS environments.

This changes the entire risk profile.

✔ Version Pinning

We lock a specific model version, configuration, and system prompt.
No silent updates. No behavior drift.

✔ Controlled Change Management

Upgrades only occur through documented change control, regression testing, and QA approval.

✔ Full Auditability

Every interaction is logged, including:

• Model version

• Prompt

• Output

• Timestamp

• User identity

This supports inspection-readiness and historical traceability.

✔ IP Protection and Data Residency

Your data never leaves your environment.
Nothing flows back into a global model.
Your intellectual property stays protected.

✔ Reduced Variability

Models can be tuned for consistency through temperature and sampling controls.
This supports use in verification and quality documentation tasks.

Conclusion: Public AI for Curiosity, Local AI for Compliance

Public AI tools are excellent for:

• Ideation

• Learning

• Early drafting

• Personal productivity

But they are not engineered for:

• Validated MedTech manufacturing.

• Stable long-term processes

• Regulatory traceability

• Controlled tool behavior

• IP protection

• Reproducible verification

For any activity involving:

• Quality records

• Verification

• CAPA

• Risk management

• Regulatory documentation

Public AI becomes a high-risk and unstable supplier.
MedTech leaders must treat it as such.

NoioMed helps MedTech companies deploy private, controlled AI ecosystems using local LLMs in secure VPS environments that align with QMSR, ISO 13485, ISO 14971, and global AI governance standards.