LinkedIn AI Training on Member Data Goes Default—How to Opt Out

Microsoft-owned LinkedIn will begin using members’ profile information, public posts, resumes and activity to train generative AI models by default in a policy change that takes effect November 3, 2025 — but a new “Data for Generative AI Improvement” toggle in Settings lets you opt out for future training if you act now.

Background / Overview​

LinkedIn’s change is part of a broader push by major platforms to fold user-generated content into the datasets that power generative AI features such as writing assistants, profile enhancers and recruiter-matching tools. The company says the update will allow its generative AI to “enhance your experience and better connect our members to opportunities,” and LinkedIn explicitly lists profile details and public posts as types of data that may be used to train models that generate content.
The update is region-aware: LinkedIn has said the change applies to additional regions (including the EEA, the UK, Switzerland, Canada and Hong Kong) starting November 3, 2025, and that private messages are excluded from training. LinkedIn also confirms that toggling the setting off prevents future use of your data for content-generation model training, but it does not undo or remove training that has already happened.
This article explains exactly what LinkedIn’s change means for users, walks through the opt-out mechanics, highlights where the legal and privacy risks lie, and offers practical guidance to professionals and administrators who need to protect sensitive data or manage compliance in business environments.

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What LinkedIn is changing — a concise summary​

• Effective date: November 3, 2025 — LinkedIn’s updated terms and support messaging state that changes go into effect on this date.
• What’s being used: Profile details, public posts, feed activity and ad engagement may be included in datasets for training content-generating models; private messages are excluded.
• Default behavior: The “Data for Generative AI Improvement” control is on by default for many accounts, meaning LinkedIn can use user data for future AI training unless members opt out.
• Opt-out scope: Turning the toggle off prevents future use of your LinkedIn-provided data for training content-generation models. It does not remove or “untrain” models that already ingested your data.

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Why this matters: practical impacts for LinkedIn users​

1. Your public posts and profile may now feed the models that generate the text you see​

LinkedIn’s generative features — things like “Rewrite with AI,” resume enhancers and suggested message drafts — are trained on corpora that now explicitly include user-provided public content and profile attributes. That means the models behind those features will increasingly reflect the stylistic and topical patterns of real LinkedIn posts. Over time, that can compress stylistic diversity and lead to more homogeneous AI-generated outputs unless the company actively counterbalances with technique-level diversity and filtering.

2. Opting out won’t erase prior contributions​

LinkedIn’s own support documentation makes this clear: toggling the setting off stops future training uses of your data, but “opting out does not affect training that has already taken place.” For professionals concerned about past content appearing in model outputs, there is no automatic undo. You can request data deletion through standard account deletion or data removal procedures, but that will not retroactively strip models trained on previously ingested material.

3. The setting is separate from other types of machine learning on LinkedIn​

LinkedIn distinguishes between content-generating generative AI training and other uses of machine learning such as personalization, ranking, moderation and security. The “Data for Generative AI Improvement” control is specifically scoped to models used to create content (e.g., suggested posts or messages), and does not necessarily change how data is used for personalization or anti-abuse systems. Users seeking broader limits may need to file additional objections or use other privacy controls.

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How to opt out (step‑by‑step)​

• Sign in to LinkedIn on the web or mobile app.
• Open Settings & Privacy.
• Go to Settings > Data Privacy > Data for Generative AI Improvement.
• Turn off “Use my data for training content creation AI models.”

Notes:

• The control stops future use of the data for model training used for content generation; you can still use LinkedIn’s AI features, but your personal data will not be added to training corpora going forward.
• The setting may be enabled by default on many accounts; users should check proactively and flip it off if they don’t want their future content used.

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Legal, regulatory and reputational implications​

Regional differences and regulatory scrutiny​

LinkedIn’s approach is regionally nuanced. Historically, LinkedIn avoided training content-generating models on EU/EEA/Swiss user data to reduce regulatory risk under GDPR and similar frameworks; the new policy indicates that the company is extending or clarifying training in more jurisdictions while offering controls and legal explanations for each region. Regulators in Ireland and across Europe have scrutinized similar moves by major platforms, and LinkedIn has been involved in inquiries and, in at least one instance, litigation alleging undisclosed training of private content.

Litigation risk and consumer complaints​

LinkedIn has faced legal action related to data disclosure and AI training practices. A 2025 class-action suit alleges that LinkedIn disclosed customer information to third parties for AI training and that opt-out options were not effectively communicated. Litigation, regulatory complaints and supervisory authority inquiries are likely to continue as platforms refine how they balance product development against privacy laws. Users and enterprises that feel harmed by how their data has been used may pursue administrative complaints or lawsuits.

The consent versus legitimate interest debate​

Companies often justify data processing either through consent or “legitimate interest” (a GDPR legal basis). LinkedIn’s messaging suggests mixed approaches: opt-out toggles and settings for user control, but in some markets the company may rely on legitimate interest for certain processing. That legal posture can be contested and may invite regulatory follow‑up, especially where marginalization of explicit, informed consent could be claimed. Expect regulators to scrutinize whether notice was adequate and whether opt-out mechanisms meet fairness standards.

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Technical realities and model behavior: what to expect​

Models will reflect the platform’s language and norms​

When models train on LinkedIn content — a mix of professional bios, job descriptions, announcements and commentary — their output will become more domain-specific: better at resume wording, LinkedIn-style posts and recruiting messages. That’s valuable for product quality, but it also creates stylistic convergence: job-seeking copy and polished professional updates generated by AI may start sounding similar across users. This is an expected side effect of training models on platform-specific corpora.

Affiliated model use and third‑party suppliers​

LinkedIn’s documentation notes that some of its models are provided by external services (for example, Azure OpenAI APIs) while LinkedIn also maintains internal models. Microsoft’s cloud and model partnerships complicate the data flow: data used by LinkedIn for training content-generation models may be shared with affiliates for development and advertising purposes in some regions. That raises questions about downstream access and replication of training datasets across different corporate entities.

Privacy-enhancing technologies are not a panacea​

LinkedIn states it will “seek to minimize personal data” in training sets and may apply privacy-enhancing technologies (PETs) such as redaction or pseudonymization. While PETs reduce direct identifiers, they are not foolproof: re-identification risks can remain when rich profile context and metadata are present. Users concerned about deanonymization or latent leakage should treat PET claims as mitigation measures rather than absolute guarantees.

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Risks and blind spots: where users and admins should pay attention​

• Retroactive exposure: Opting out is forward-looking — it won’t remove your past contributions from models already trained. If you’ve ever posted a CV, public job application detail, or detailed project description, that content may already be embedded in model weights.
• Hidden defaults: Because the toggle is often on by default, many users will be opted in without actively consenting; that pattern has proven controversial across platforms and can trigger regulatory complaints.
• Scope creep across affiliates: Data shared for model training in one part of a corporate family can be reused for other product lines, influenced by affiliate sharing clauses; read the updated terms carefully if you want to understand downstream uses.
• Business/enterprise complexity: Company accounts, applicant-tracking system integrations and enterprise hires may involve different contractual data flows. Administrators should audit vendor contracts and tenant settings to ensure organizational data is handled in accordance with corporate policy and compliance obligations.
• Model outputs that mirror sensitive data: In rare cases, generative models trained on public content can reproduce personally identifying details or verbatim phrasing. While LinkedIn excludes private messages from training, public content with sensitive specifics can still appear in aggregated outputs. Treat model outputs as potential leak vectors if your posts contain proprietary or sensitive information.

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How to reduce risk — practical checklist​

• Toggle the setting off now: Settings > Data Privacy > Data for Generative AI Improvement > turn off “Use my data for training content creation AI models.” Do this before November 3, 2025 if you want to stop future use.
• Audit public posts: Remove or edit public posts and profile sections that contain sensitive, proprietary, or unusually detailed personal data if you do not want that material potentially influencing model behavior going forward. Removing content will prevent new ingestion but does not erase the past.
• Use data deletion mechanisms if necessary: LinkedIn’s data deletion or data removal forms can remove specific data from your account; that is the formal route if you need content taken down, but it does not guarantee the training datasets will be purged retroactively.
• For enterprises: review vendor contracts and admin consoles. Ensure tenant-level controls and contracts with LinkedIn or Microsoft explicitly address whether enterprise content may be used for generative AI or shared with affiliates. Use legal/IT channels to file objections or formal requests where appropriate.
• Monitor regulatory developments: Watch supervisory authority guidance (e.g., data protection agencies and courts) because regulator rulings can change what companies are allowed to do with user data and may require new opt-in/notice mechanisms.

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How credible are LinkedIn’s promises — and what remains unverifiable?​

LinkedIn and Microsoft have published support documents and blog updates describing scope, regional exceptions and opt-out mechanics. Multiple independent outlets — including major tech press and legal reporting — have corroborated the broad outlines: the toggle exists, private messages are excluded, and opting out only stops future training. That said, several operational claims remain difficult for outside observers to verify, including:

• The exact amount and nature of data redaction applied before training (LinkedIn says it will “seek to minimize personal data,” but methods, parameters and effectiveness aren’t public). Treat such claims as mitigation promises rather than demonstrable outcomes. Caution: these are assertions that cannot be fully audited by end users.
• Whether and how training corpora are replicated or retained by affiliates after initial ingestion — terms and implementation vary and are harder to verify from outside the company. Caution: affiliate-sharing language means downstream reuse is possible, but the precise scope is not externally auditable.

When companies invoke “privacy-enhancing” techniques, independent verification is the only way to convert assurances into trust. Absent audits or published technical details, such claims should be treated with healthy skepticism.

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Context: where LinkedIn’s move fits in the industry trend​

This change follows a broader industry cycle where platforms that paused or restricted European training of AI models have been revisiting those policies and, in some cases, resuming training with updated legal arguments and opt-out mechanisms. Regulators and privacy advocates have taken differing positions: some supervisory bodies have allowed responsible reuse of public content for AI training under certain legal bases, while advocacy groups have continued to litigate or file complaints. Expect LinkedIn’s update to be debated in that same context.
Historically, LinkedIn has also leveraged Azure OpenAI services and other Microsoft-hosted model providers for some features; the platform combines external large language models with its own product-specific models. That technical hybridity is important because it shapes how data flows between LinkedIn, Microsoft cloud services and model providers. Users should assume that content used for model improvement can flow into multi-vendor pipelines unless explicitly constrained by region, contract, or regulation.

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Bottom line: a pragmatic stance for professionals​

• If you value strict control over how your writing and profile are used to train AI, turn the Data for Generative AI Improvement toggle off now; it blocks future use but not prior uses.
• Treat LinkedIn as a public publishing platform by default: anything public can become training material for models that generate public-facing text and recruiter or sales outreach. Edit or remove content you wouldn’t want used as training fodder.
• For organizations and privacy officers, perform a contractual and technical audit of LinkedIn integrations, applicant-tracking workflows and recruiter tools to confirm whether enterprise data is subject to separate terms or protections.

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Conclusion​

LinkedIn’s November 3, 2025 policy update formalizes a trade-off seen across the tech industry: better, more tailored AI features powered by real platform content at the cost of broader corporate access to user-generated material. The company offers an opt-out for future training and excludes private messages, but the default-on nature of the control plus the explicit caveat that opting out does not erase prior training are meaningful limitations for users who care about retroactive privacy.
The reasonable immediate response for LinkedIn members is straightforward: check the Data for Generative AI Improvement toggle in Settings > Data Privacy and decide whether you want your future posts and profile to contribute to the models that write the very suggestions and templates you may use. For organizations and privacy teams, this announcement is a cue to audit integrations and update policies so that employee and applicant data remains handled according to enterprise compliance expectations.

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Source: Windows Latest Microsoft's LinkedIn warns it will auto train AI models on your data, but you can opt out