Yes — if you are in a region where LinkedIn offers the control, you can switch off the setting that lets the platform use your posts and other member data to train its generative AI models, and the switch lives in your account's data privacy settings rather than anywhere near your feed.
The important caveat is that turning it off stops future training use; it does not reach back into a model that has already been trained. So the setting is best understood as a forward-looking control, not an eraser.
Let me be careful about what I can actually verify here, because this is a topic where confident-sounding blog posts are everywhere and most of them are repeating each other. I have not logged into a LinkedIn account, checked the current menu labels, or read the live policy page, so I am not going to quote an exact toggle name, a date a policy changed, or a claim about which countries get the option.
Those details shift, and the only reliable source for them is LinkedIn's own settings screen and its help centre, checked on the day you read this. What I can explain is the mechanism underneath, which does not change even when the labels do.
Here is the mechanism, and it is the same across almost every large platform. When you publish a post, a comment, or a long-form article, you are creating text. Text is the raw material for training a large language model.
A model learns by being shown huge volumes of examples and adjusting its internal weights — the numerical dials that shape its output — so that it gets better at predicting what comes next. Your post is one of those examples. Deleting the post removes it from the public web and from your profile, but if it was already swept into a training run, its influence is baked into the weights.
There is no per-post undo button, because the model does not store your post as a neat labelled file. It stores a statistical adjustment spread across millions of dials. This is why "I deleted it afterwards" and "it was never used" are genuinely different states, and why the timing of your decision matters more than the decision itself.
That leads to the decision rule I would actually give someone. Before you post something sensitive — a client's unreleased product name, a medical detail about a family member, a draft of a contract clause, a candid note about a colleague — ask one question: would I be comfortable if this text shaped a model I cannot inspect?
If the answer is no, do not post it and then switch off a setting. Do not post it at all. Put it in a document, an email, or a private message.
The setting is a reasonable hygiene step for the general run of your posting, but it is not a substitute for that judgement call, because a setting change today cannot retroactively remove text you published last year. For the everyday content most people post — opinions, links, job updates — the practical risk is low and the setting is mostly about your comfort level.
For regulated work, client material, or anything under an NDA, the rule is simpler: it does not belong in a post.
A few honest limits. First, the setting controls one use of your data; it does not stop LinkedIn from using your content to show you ads, rank your feed, or recommend you to recruiters, which are different systems with different purposes. Second, if you are outside the regions where the control is offered, you may not see the toggle at all, and your practical options shrink to not posting the sensitive thing.
Third, "off" is not the same as "deleted" — the platform may still retain your content under its own retention rules even when it is not feeding a model. Fourth, and this catches people out: the same logic applies to every AI tool you paste text into, not just social platforms. If you drop a confidential email into a chatbot to have it summarised, you have made the same trade, often with fewer controls.
If you want a broader walkthrough of that pattern, our guide on how to use AI with your privacy intact covers the general habit, and the piece on whether AI tools can really leak your private data digs into what leaking actually means in practice.
One tip that goes beyond the obvious: audit before you toggle. Most people flip the setting and feel done, but the posts that matter are the ones already published. Search your own profile for the words you would least want in a training set — client names, project codenames, anything under confidentiality — and decide whether those specific posts should come down. That is a ten-minute job and it is worth more than the switch itself, because the switch only governs what happens next.