Can Pharma and Biotech Teams Use ChatGPT and Claude Safely? A Practical Checklist

In short: Yes, with rules. The risk is mostly about which data goes into which tool, not about the tool being "AI". Sort your data into green, yellow and red; use only approved business-grade tools for anything beyond public information; keep a human responsible for every output; and write the rules down in one page. A checklist and a drafting prompt are below. This is practical guidance, not legal advice.

The question your team is already asking in the hallway

Someone on your team is already using a chatbot. Maybe they paste a paragraph from an unpublished manuscript to tighten the English, or a table of results to ask what stands out. They aren't careless. They are being efficient with a tool nobody has told them how to use.

Banning AI outright rarely works; it pushes the use onto personal accounts where nobody can see it. A short, clear policy works better. This article gives you the pieces to build one.

Where the real risks are

• Confidentiality and IP. Unpublished data, sequences, compound structures, strategy documents and anything under an NDA. Once pasted into a tool, you depend on that vendor's terms for how it is stored and used.
• Patient and personal data. Identifiable or re-identifiable information has privacy rules attached. Pasting it into a consumer tool is a common and avoidable mistake.
• Accuracy. Models state wrong things confidently, including invented references and numbers. In research and regulated work, an unchecked output is a liability.
• Traceability. In regulated environments you may need to show how a document or analysis was produced. "Someone asked a chatbot" is not a record.
• Shadow use. Unapproved personal accounts are the biggest practical gap, because they are invisible to IT.

Sort your data: green, yellow, red

A three-color rule is easier for people to remember than a policy document. Adapt the examples to your organization.

• Green: fine in an approved tool. Public papers, published protocols, general questions, text you wrote with no confidential content, brainstorming and drafts about public topics.
• Yellow: only in a business-grade tool your organization approved, and ideally with a reviewer. Internal documents with no sensitive content, early drafts of your own unpublished writing, de-identified or aggregate material, meeting notes.
• Red: not in any general AI tool without explicit sign-off. Identifiable patient or participant data, unpublished results, sequences or structures with IP value, data under NDA or from a partner, anything your contracts restrict.

What to check before approving a tool

Terms differ between a free consumer plan and a business or enterprise plan from the same vendor, and they change over time. Ask the vendor and read the current documents rather than rely on what you remember.

• Is our input used to train the vendor's models? Under which plan is it not?
• How long is data retained, and can we control or delete it?
• Does the plan offer admin controls, single sign-on and audit logs?
• Where is data processed and stored, and does that meet our contracts?
• Is there a data processing agreement, and a business associate agreement if health data is in scope?
• What does the vendor's security documentation say (independent audit reports, for example)?
• Can we turn off features we don't want, such as web browsing, plug-ins or memory?

Rules for people, not just tools

1. Use only the approved tool for work content. No personal accounts for company material.
2. De-identify first. Strip names, IDs, dates and anything that could point back to a person or a project, then paste.
3. A human owns the output. Whoever submits or signs the document is responsible for every statement in it.
4. Verify before you reuse. Check numbers, citations and quotes against the source.
5. Record significant use. For anything that feeds a regulated document, note the tool, the date, the purpose and who reviewed it. Check with your quality team what your processes require.
6. Report mistakes early. If something sensitive went into the wrong tool, a quick report is better than a quiet cover-up.

A one-page policy you can draft today

Use a model to produce the first draft, then hand it to the people who own the decisions. Do not paste any real policy text, contracts or confidential details into it.

Draft a one-page AI-use guideline for a [pharma / biotech / academic research] team of about [number] people.
Include: purpose; approved tools (placeholder: [list]); a green/yellow/red data table with examples relevant to [our work]; five rules for individuals; when human review is required; how to log AI use in [documents we regulate]; who to ask when unsure ([role]); how to report a mistake.
Plain language, no legal claims, and mark every place that needs review by legal, privacy, IT security or quality with [REVIEW: team].

In the workshops I run for research and pharma teams, I use a framework called S.A.F.E. for exactly this: a repeatable way to decide what to share with a model and how to check what comes back. The habit matters more than the acronym. People who sort the data first make fewer mistakes.

What to do this week

• Ask your team, without blame, which AI tools they already use and for what.
• Choose one approved business-grade tool and make it easy to access.
• Publish the green/yellow/red table and the six rules.
• Review the first month of use and adjust.

When the tool is approved, the next problem is quality: getting accurate, checkable output. See How to Write Good AI Prompts for Scientific Work and AI Literature Review Without Fake Citations.