AI governance in life science companies

Where AI actually enters the company

Most of the public worry about AI in life sciences points at the product: a model inside a device, an algorithm in a trial. The faster, quieter adoption is internal. Across quality, regulatory affairs, clinical operations, safety, and the ordinary back office of HR, finance, and legal, employees are reaching for LLMs to get through paperwork.

That internal layer is where the company's regulated records, trade secrets, and personal data actually sit, and it is usually where governance is thinnest. The tool is whatever someone signed up for, the prompt and the output are gone the moment the tab closes, and nobody owns the question of what the model was allowed to touch.

Internal records are still regulated records

A lot of GxP paperwork is administrative: standard operating procedures, deviations, CAPAs, validation documents, training records, the drafts that eventually become a submission. These are regulated records even though no patient ever sees them. Under 21 CFR Part 11 in the US and Annex 11 in the EU, any system that creates or changes them has to enforce access controls and keep a complete, tamper-evident audit trail. The FDA's 2018 data integrity guidance sums the expectation up as ALCOA: Attributable, Legible, Contemporaneous, Original, and Accurate, later extended to Complete, Consistent, Enduring, and Available.

Picture an associate drafting a deviation writeup or a SOP revision in a consumer chatbot. Nobody recorded which tool was used, which model version answered, what source it drew from, or who reviewed the text before it entered the quality system. In a GxP context that absence is itself the finding.

The FDA wrote that guidance because inspectors kept turning up records nobody could account for. An unlogged AI just produces those same orphan records faster.

The exposure goes beyond GxP

The administrative side is also where data leaks. An employee pastes a draft protocol, a formulation, a partner's data, or a trial subject's information into a public model to summarize or tidy it up, and that text is now outside the company with no record it ever left. In one study of enterprise use, roughly 11% of what employees pasted into tools like ChatGPT was confidential business data. In pharma the thing pasted is often a trade secret or someone's health information.

It reaches the corporate functions too. The EU AI Act classifies AI used to screen job applicants, evaluate staff, or monitor performance as high-risk, with obligations for transparency and human oversight and a duty to tell candidates when an AI weighed on a decision about them. A recruiting team that quietly runs resumes through a model is already inside that scope, usually without the controls the rule assumes.

Guardrails aren't the fix

The usual answer is guardrails: content filters, a confirmation dialog, an automated review step. But these are statistical patches on a statistical system, so they fail the same quiet, confident way the model does. People click through confirmation dialogs without reading them, and a model reviewing another model just adds a second component with the same failure mode.

What a logged decision looks like

Governance has to be structural: a request goes to an agent that one department owns, scoped to the data and tools that department already answers for. Everything it does is written to a policy-bound log, the way a batch record captures a manufacturing step, so months later you can still trace what was asked, which model version answered, and who approved the result. The people accountable for a function can watch how AI behaves inside it before an auditor or a security review asks.

AI belongs in this industry; it can pull weeks out of the internal grind. We built LogicNerve around the failure we worry about most, the quiet one: an employee runs a regulated document or a confidential file through whatever model is open, nobody records it, and the gap surfaces only when an inspector or a breach investigation starts pulling the thread.

Every request runs through an approved, department-owned agent, and every action it takes is policy-controlled and recorded. Get in touch to see how LogicNerve can help your organization adopt AI responsibly.

Sources

21 CFR Part 11: Electronic Records; Electronic Signatures (eCFR)

EudraLex Volume 4, Annex 11: Computerised Systems (European Commission)

FDA: Data Integrity and Compliance With Drug CGMP, Questions and Answers (2018)

EU AI Act, Annex III: High-Risk AI Systems (including employment and worker management)

Cyberhaven: How employees are putting confidential company data into ChatGPT