In 2025, building your own AI model is not just a technical decision — it’s a legal one. Whether you’re developing a language model for enterprise search or training an image recognition system for retail analytics, the question of AI model IP clearance stands between innovation and potential business litigation.
The legal risks aren’t hypothetical. Multiple lawsuits are underway targeting AI developers for using copyrighted content in training datasets, from Getty Images vs. Stability AI to The New York Times vs. OpenAI. Meanwhile, regulators in the U.S. and EU are exploring how intellectual property law applies to synthetic media. For technology startups, failing to conduct proper IP clearance before training or releasing a foundation model can expose the company to claims of infringement, data misuse, and even unfair competition.
Why AI Model IP Clearance Is a Legal Priority
Before training your own LLM, image model, or multimodal system, your team must evaluate the legal status of the datasets involved. This is the cornerstone of AI model IP clearance. Language models like GPT or LLaMA require billions of tokens scraped from books, blogs, articles, and forums. Vision models ingest datasets of labeled images, often containing people, products, logos, and other proprietary assets. If any portion of this training data is protected by copyright or trademark law — and used without proper authorization — the resulting model may be considered “tainted.” Courts have not fully resolved the scope of fair use in this context. But in current litigation, plaintiffs argue that model training is a form of commercial use and thus requires a license.
For example, using proprietary datasets such as image libraries, medical texts, or commercial news archives without permission can trigger litigation. Using trademarked images or branding (such as product packaging or fashion designs) in vision datasets may also lead to trade dress infringement claims, especially if your model enables outputs that resemble or replicate those brands.
At L.A. Tech and Media Law Firm, we advise clients building proprietary AI models to clear their datasets proactively. That means understanding the source of the data, the scope of use rights, and how that data might produce outputs with legal implications. This process is now as critical as security audits and bias testing.
Best Practices for AI Model IP Clearance in Training Pipelines
Effective AI model IP clearance starts with data provenance — knowing where each part of your training data came from. That includes confirming whether the source is:
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In the public domain
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Openly licensed (e.g., Creative Commons with commercial use rights)
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Proprietary but licensed under agreement
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Scraped from third-party sites without explicit permission
Many startups rely on datasets shared via academic or open-source communities. But “open” does not mean “license-free.” For example, even Creative Commons licenses come with conditions, such as attribution or non-commercial restrictions. Using non-commercially licensed data to train a commercial AI product may violate those terms.
When possible, AI developers should use first-party data — customer interactions, proprietary documents, or licensed partner content — to fine-tune or train models. Alternatively, firms can license commercial datasets, such as those curated by publishers, media companies, or specialized dataset providers, with express legal agreements.
Image models carry their own risks. If you’re training a visual recognition tool, ensure that photos, logos, or products within your dataset are not subject to active copyright or trademark rights. Scraping branded content without permission can lead to claims under the Lanham Act or Digital Millennium Copyright Act (DMCA), particularly if the model is used to generate or identify branded visuals.
AI Model Lawyer
AI Model LawyerEven if your training data appears lawful, the outputs of your model can still raise legal red flags. That’s why AI model IP clearance includes post-training audits. For language models, this includes testing whether the model can reproduce copyrighted text verbatim or generate outputs that mimic the style of specific publications. For image models, it involves confirming that outputs don’t replicate trademarked logos, fashion designs, or copyrighted works.
Some courts have held that reproducing or generating “substantially similar” content can constitute copyright infringement, even if the original material was never directly copied during training. Others are weighing whether the internal weights or latent space of a model that used copyrighted data can be considered derivative.
The legal standard is evolving, but the direction is clear: AI developers who skip IP clearance are taking measurable, escalating legal risks. The smartest approach is to treat dataset sourcing and model behavior as part of your compliance stack. At minimum, that means maintaining documentation of where data came from, how it was cleaned, and what licenses were applied. For regulated industries or commercial deployments, legal opinions and indemnity clauses may also be advisable.
David Nima Sharifi, Esq., founder of L.A. Tech and Media Law Firm, advises AI companies, developers, and investors on intellectual property clearance, licensing, and litigation strategy. Featured in the Wall Street Journal and recognized by the Los Angeles Business Journal as one of the Top 30 New Media and E-Commerce Attorneys, David has worked with some of the most forward-thinking teams in AI, health tech, and software.
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