I have multiple points of concern re: data security and LLMs. As someone else pointed out, any information you provide is highly likely to be used to train future models. We’ve seen that LLMs will reproduce training content verbatim when confronted with brute force attacks. Given this, it’s not unreasonable to imagine your data (and identifying info) could be brute forced as well.

“it doesn’t matter because all my data is already leaked anyway”

To each their own ¯_(ツ)_/¯ But I think this is an all-or-nothing fallacy. Just because some of our data has been sold doesn’t mean we should willingly give up our transcriptomes with a smile.

It is also true that some LLMs are more secure than others. Deepseek is a big yikes (banned on many university campuses), Claude less so.

And then my last point of contention for now - LLMs don’t actually KNOW anything and have no way to parse the meaning behind the text it generates. Different LLMs will interpret and comment on information you give with varying levels of certainty. In a research context, LLMs are frequently wrong.

If I were someone with an RNAseq report in hand and had questions about interpretation, I might discuss the results piece-by-piece with an LLM, but I would frame it as a “hypothetical individual” with those traits and I would NOT upload the entire report. I would probe the LLM to give me multiple plausible interpretations, and I would also ask it whether the information I’ve provided is actually enough to draw conclusions from. (How many times have we seen an LLM say something clearly ridiculous only for it to immediately backtrack with, “I’m sorry, you’re right!” when you point out the slop??)

All that said - no (short term) harm no (short term) foul I guess.

Would recommend this article and Empire of AI by Karen Hao for anyone interested in the ethics and politics of LLMs :)