LinkedIn AI Training Expands to EU Regions by Default — Opt Out Now

LinkedIn will start using public profile data from members in the European Union and other regions to train its generative AI models — a change that goes live on November 3, 2025 and is enabled by default unless users take action to opt out.

Background​

LinkedIn’s parent company, Microsoft, has steadily folded generative AI features into the professional network’s product suite over the past two years. The platform says the purpose of the new data policy is to improve content-generation features — from profile-writing assistants to recruiter matching and job-description drafting — by training the underlying models on real-world, professional content hosted on LinkedIn. The update is region-aware: it extends previous US policies to include the EU/EEA, Switzerland, Canada, Hong Kong, and several other markets beginning November 3, 2025. This is part of a wider industry pattern. Major platforms including Meta and Google have adopted similar programs that rely on public user content for AI model training, often making the option available by default and offering opt-out routes rather than explicit opt-in consent. That pattern has triggered regulatory scrutiny and legal challenges across Europe and beyond.

---

What LinkedIn says it will use — and what it won’t​

LinkedIn’s published guidance and support documentation describe the scope of data that may be used for training generative AI models. The company has identified a set of public, account-level inputs it may ingest, including:

• Profile details (headline, summary, work history, education, skills and endorsements).
• Public posts, articles, comments and polls.
• CVs, resumes or job application details submitted through LinkedIn.
• Public activity signals such as feed interactions and public endorsements.

LinkedIn has stated that certain categories will not be used for generative AI training, most notably private messages and (in the company’s public messaging) salary data submitted through non-public channels. The company has also said accounts belonging to minors will be excluded from training datasets even if their account settings might otherwise allow it. Those exclusions matter, but they do not eliminate privacy risk. Public posts and CVs often contain highly sensitive professional information — project names, employer strategies, contact details and, in some cases, personally identifiable data that people might reasonably assume is used only for recruiting or networking. Once that material is used to train a model, it becomes much harder to treat it as "private" again.

---

The opt-out mechanics: how it works (and the limits)​

LinkedIn will turn on a setting called Data for Generative AI Improvement by default in affected regions. If members do not change the toggle to off before the effective date, future content they publish and certain profile data will be eligible for ingestion into training datasets. To stop future use, users must actively change this setting in Settings & Privacy under Data Privacy. Turning it off prevents future model training on your data; LinkedIn and independent reporting make clear that the toggle is not retroactive — data already used to train models cannot be untrained by flipping your setting. Practical steps to opt out (desktop and mobile):

1. Open your LinkedIn account and click your profile picture (Me menu).
2. Select Settings & Privacy.
3. Click Data privacy (left-hand menu).
4. Under “How LinkedIn uses your data” select “Data for Generative AI Improvement.”
5. Toggle “Use my data for training content creation AI models” to OFF.

Do this now if you prefer not to have your future LinkedIn content used to train generative AI models. For users who want to pursue stronger remedies — such as removal of specific items or challenging the legal basis for processing — LinkedIn points to regional objection forms and data-processing objection mechanisms, though those routes may be slower and produce variable outcomes depending on national regulators.

---

Legal basis and the regulatory landscape in Europe​

LinkedIn has described its legal basis for processing user data for model training as legitimate interest under the GDPR in many jurisdictions. That legal basis allows data controllers to process personal data without explicit consent when they can demonstrate a lawful, documented reason and show that the processing does not override the rights and freedoms of data subjects.
European regulators have not left that question unexamined. The European Data Protection Board (EDPB) published an opinion addressing when AI model development can rely on legitimate interest, and it set out a three‑step test and a series of mitigations (documentation, minimisation, demonstrable necessity, and risk assessments) that supervisory authorities should expect to see. National regulators like France’s CNIL have also issued practical guidance explaining how legitimate interest may be applied — but with strict caveats: companies must evaluate necessity, apply privacy-enhancing techniques, document their Data Protection Impact Assessments (DPIAs), and be ready to justify why less intrusive alternatives (synthetic data, restricted datasets) would not be sufficient. This regulatory backdrop means that reliance on legitimate interest is plausible but not unassailable. Privacy advocates and enforcement bodies have repeatedly signalled that automatic opt-in and broad reuse of public content for commercial model training is a flashpoint for enforcement and litigation.

---

Why the legal argument matters — practical implications​

Relying on legitimate interest shifts the burden to the controller (LinkedIn/Microsoft) to document necessity, balance tests and safeguards. For users, the difference between legitimate interest and explicit consent is meaningful:

• Consent gives users affirmative control and usually must be freely given. Opt-outs, by contrast, place the burden on users to assert their preferences.
• Legitimate interest requires a documented balancing test that regulators can audit; if regulators find the test flawed or incomplete, processing can be ruled unlawful and enforcement actions can follow.
• Even where legitimate interest is accepted, national regulators can and do impose operational requirements (retention limits, stricter DPIAs, or prohibitions on certain data classes).

Regulatory activity in 2024–2025 demonstrates these risks are real. Advocacy group litigation and supervisory complaints are already in motion against other platforms that used the same legal route to justify EU data for AI training. Those disputes show the legal theory can succeed in some contexts but also that enforcement is active and outcomes are uncertain.

---

Technical and privacy risks — what engineers and privacy teams should ask​

Training models on platform data can be done in many ways, and LinkedIn’s public assurances reference privacy-enhancing techniques (pseudonymisation, minimisation and other mitigations). Those techniques reduce risk, but they are not absolute cures.
Key technical questions that should be asked and — where possible — answered with public documentation or independent audits:

• How is minimisation implemented at ingestion time? Are certain fields stripped before storage, or are raw records retained until later filtering?
• What checks are run to detect memorisation or verbatim leakage from models (prompt-based extraction tests, example probing)?
• Where are datasets stored, who has access, and how long are raw and processed datasets retained?
• How are affiliate-sharing and downstream reuse managed? If LinkedIn shares training corpora or model checkpoints with other Microsoft entities, what contractual and technical boundaries exist?
• How does LinkedIn ensure that removing or deleting a post or CV from a profile stops future ingestion and that deletion requests are logged and audited?

Without published technical detail or third‑party audits, claims about effective redaction and non-reversibility remain assertions rather than verifiable facts. That is an important caveat: companies can and do implement privacy-preserving pipelines, but external observers should treat non‑audited statements as mitigations, not guarantees. (This is an area where regulators and independent auditors could add value by requiring and validating documentation.

---

Business impact: for professionals, recruiters and enterprises​

The change is not just a consumer‑privacy story — it has real ramifications for hiring teams, in‑house counsel, talent acquisition platforms and companies that integrate LinkedIn data into HR workflows.

• Recruiters who use LinkedIn’s AI features may see improvements in job-matching and candidate outreach, powered by models trained on real candidate profiles. That could increase efficiency, but it also raises questions about bias amplification if models learn patterns tied to protected characteristics.
• Employers that require staff to maintain LinkedIn profiles should update internal policies. Organizations must decide whether employee accounts should be opted out to reduce corporate risk or left on to benefit from AI features.
• Applicant Tracking Systems (ATS) and third-party integrations that expose applicant CVs to LinkedIn may create mixed legal bases: personal data provided to an ATS by an applicant could be processed under contractual necessity or consent, and when that data transits to LinkedIn it may be processed under different legal grounds. Contracts and data-mapping exercises should be revisited.
• Companies with global workforces must manage inconsistent regional rules and settings. For example, a multinational employer may need to treat EU-based employees differently from U.S.- or India-based employees to stay aligned with local law and with platform defaults.

Enterprises should conduct a quick internal audit: inventory LinkedIn integrations, review administrative account controls, and decide whether tenant-level or employee-level opt-out is appropriate. The default-on nature of the setting increases the likelihood that corporate accounts or employee-shared materials could be incorporated into training datasets unless proactively managed.

---

Practical checklist: what users and admins can and should do now​

• Toggle the setting off if you do not want future LinkedIn content used for generative AI training: Settings & Privacy > Data privacy > Data for Generative AI Improvement > OFF. This prevents future ingestion but does not undo past training.
• Audit public content: remove or redact posts, project descriptions, CV entries and attachments that contain sensitive or proprietary information.
• For recruiters and enterprises: run an inventory of LinkedIn integrations and review service agreements to determine what data flows to LinkedIn and whether corporate policies should require employees to opt out.
• If you are subject to EU law and want stronger protection, consider filing a Data Processing Objection through LinkedIn’s regional mechanisms and keep records of formal requests.
• For high-risk profiles (executives, R&D leads), consider more aggressive steps: remove detailed project descriptions, avoid posting full CVs publicly, and use private channels for sensitive discussions.

These steps will not make past training disappear, but they control forward exposure and reduce the sources of new training material.

---

What remains unverifiable — and where regulators or auditors should focus​

LinkedIn and Microsoft have made public assurances, but several operational and architectural claims cannot be validated without independent review. These include:

• The robustness of redaction and de-identification pipelines prior to training.
• The existence and results of internal leakage tests designed to detect whether models can reproduce verbatim content.
• The scope and governance of affiliate sharing (how datasets, model weights or derived artifacts are reused across Microsoft entities).
• The efficacy of procedures to honour deletion or objection requests at the model level.

Regulators and industry auditors can add value by requiring documentation, DPIAs and, where appropriate, independent technical audits. Transparency reports that include high‑level metrics (number of records ingested, proportion of datasets pseudonymised, number of objection requests processed) would help convert corporate assurances into verifiable governance. Until then, some of these claims must be treated as operational promises rather than independently verified facts.

---

Broader policy context: AI governance and the EU’s evolving rules​

The LinkedIn move lands in an era of rapidly evolving AI governance. The EU’s regulatory apparatus — including the EDPB, national Data Protection Authorities and specialist AI offices — is actively shaping how personal data can be used in model training. The EDPB’s opinion on model training and the CNIL’s technical guidance are notable steps toward harmonising practice and clarifying expectations, but enforcement and interpretation will still vary by country.
At the same time, corporate decisions about default settings, documentation and opt‑out design patterns are being judged not only on legal compliance but on consumer trust and reputational impact. Several high‑profile complaints and legal actions against other platforms demonstrate that public backlash and litigation risk are real and can produce material outcomes for companies that move too fast without clear, documented safeguards.

---

Conclusion — a pragmatic reading​

LinkedIn’s decision to extend AI training to EU and other users by default is consistent with a broader industry pattern: platforms increasingly rely on the scale and realism of user-generated content to improve generative features. That strategy offers tangible product benefits — better job matches, more useful writing tools and more contextual professional suggestions — but it also raises well-known privacy, legal and technical risks.
The most important near-term takeaway for professionals is simple and actionable: if you do not want your future LinkedIn content used to train generative AI, flip the Data for Generative AI Improvement toggle to off now. Organizations should treat this as a governance moment — audit integrations, revisit employee guidance and ensure legal teams have assessed contract and compliance implications.
Longer term, the question is whether legitimate interest combined with opt-out controls will withstand regulatory scrutiny and public expectations. That will depend on how well companies document necessity, implement technical safeguards, and allow independent verification of their assertions. Until such transparency and independent validation become standard, users and corporate administrators should adopt a cautious posture: assume public content can be reused for model training unless explicitly restricted, and take immediate steps to limit exposure where confidentiality matters.

---

Source: The Brussels Times LinkedIn to use data from its users in the EU to train AI