AI doesn't run on magic. It runs on data.
If you're building models, integrating with OpenAI, or launching a SaaS product that processes user input—you're relying on data to power your technology, shape your outputs, and train your systems. Here's the risk: if your AI system is trained on data you don't own—or can't legally use—you could face lawsuits, investor red flags, or regulatory action. And most teams don't realize this until it's too late. And that's where the legal exposure begins.
Whether it's scraped from the web, pulled through an API, uploaded by users, or purchased from a third-party source, the data you use may come with strings attached. This includes copyright restrictions, privacy obligations, or license terms that aren't obvious until it's too late.
This article breaks down the core legal issues around AI data ownership and what today's AI startups, product leads, and legal counsel need to watch for when handling third-party data or building on top of someone else's platform. Because the question isn't just "Can we use this data?"
It's: "What happens if we use it wrong?"
Data Rights 101 – What Can Be Used to Train AI?
No matter how advanced your model is, it's only as clean as the data pipeline behind it. And when it comes to using data in AI systems, not all sources are created equal.
First-Party Data & Third-Party Data
First-party data: the kind your company collects directly through apps, user interactions, or internal tools is typically the safest to use. If your privacy policy covers it, and users have consented to how their information is handled, you're on solid ground for most internal use cases, including AI training.
But third-party data is where most of the risk lives. This includes everything from public websites and open-source code to datasets you buy or pull through APIs. Many of these sources appear free or open, but legally, they may still be protected by copyright or license terms not immediately obvious.
Consider the ongoing GitHub Copilot litigation. GitHub trained its AI assistant on millions of public code repositories. But open-source doesn't mean no-strings-attached. The lawsuit argues that using licensed code without proper attribution or beyond license terms still violates copyright law, even if the data is public. For startups training on scraped or licensed content, this case set off alarm bells for good reason.
The core issue? Public ≠ free use. And unless you've reviewed the license or terms, you may not have the right to use third-party data at all; especially for training or commercial deployment.
API Outputs: Don't Skip the Fine Print
Even APIs come with fine print. Many providers, including OpenAI and Google, limit how you can store, analyze, or reuse the data their tools return. Some expressly prohibit using outputs for model training or retaining customer data beyond a defined window. If you're building workflows that rely on API results, it's essential to check those terms before turning them into training material.
Personal Data: Where Privacy Law Applies
The same caution applies to personal data. Laws like the GDPR and CCPA impose strict rules on how companies collect, store, and process user information; especially if it's used for profiling, personalization, or automated decision-making. If your dataset includes customer prompts, user uploads, or anything that could identify a person, you may need to implement consent protocols, anonymization steps, or deletion mechanisms to stay compliant.
The bottom line: It's not enough to have data. You need to know what rights come with it, and what obligations follow.
Model Ownership vs. Data Ownership
Owning a trained model doesn't mean you own what went into it.
This is one of the most overlooked and most consequential misunderstandings in the AI space. You might hold the IP rights to the model architecture, the weights, even the outputs. But if your training data came from third-party sources, your rights may be limited by the original license or terms.
Many open datasets, for example, carry "no commercial use" clauses. Others restrict redistribution, derivative works, or require attribution. If those terms weren't respected during training, your model may be sitting on shaky legal ground, especially if it's deployed commercially or licensed to others.
This disconnect becomes even riskier in M&A deals or fundraising. If you can't clearly document what data was used, who approved it, and under what terms, it can trigger investor hesitation—or worse, unwind a deal in diligence.
The solution is simple but often skipped: maintain a data provenance audit trail. That means tracking:
- Where each dataset came from
- Whether it was licensed, purchased, or scraped
- What terms or restrictions apply
- Who signed off on its use
Clear documentation is your best defense if questions ever arise about the legality of your model's foundation.
More importantly, it shows that your company takes AI data ownership seriously, something regulators, partners, and customers are watching more closely every quarter.
What Third-Party API Users Need to Know About Data Responsibility
Using APIs Means Owning the Risk
Integrating with tools like OpenAI, Claude, or Google Vertex is fast, powerful, and deceptively simple. But when your product sends customer data through someone else's API, you're not just building features. You're accepting liability.
Even if the API provider claims not to store or train on your inputs, that doesn't absolve you of responsibility. Under laws like the GDPR, CCPA, and HIPAA, you're still accountable for how user data is collected, processed, and protected. That includes what happens once it leaves your platform and enters someone else's system.
Compliance Isn't Outsourced
Security expectations are rising as well. Frameworks like SOC 2, ISO 27001, and NIST require more than internal safeguards—they expect you to vet and document the practices of your vendors. If an API call exposes sensitive information, or if data is logged without proper authorization, you're the one who has to answer for it. Regulators and customers won't distinguish between your code and the third-party systems you rely on.
This is where many companies get tripped up. Just because an API provider says they don't retain user data doesn't mean your use is compliant. If you fail to disclose how that data is routed, or if encryption isn't properly configured, you may still be violating privacy laws—or your own user agreements.
Contracts and Documentation Matter
To reduce risk, companies should treat data-sharing APIs like any other processor relationship. That starts with Data Processing Addendums (DPAs) or broader AI website agreements. A well-structured DPA lays out how data is handled, where it flows, what the provider can and can't do with it, and how issues like deletion, breach response, and audit rights will be managed. If you're passing personal or regulated data through an AI API, a DPA is not optional; it's your first line of defense.
It's equally important to request and retain documentation on the provider's internal practices. You need clarity on how long data is stored (if at all), how it's encrypted in transit, whether logs include user content, and what guarantees exist around deletion. This isn't just about compliance, but about building systems that users, partners, and investors can trust.
In the AI space, data responsibility doesn't stop at your infrastructure boundary. The moment you hit "send" on a third-party call, your legal exposure continues until you've closed the loop, contractually, technically, and operationally.
Legal Obligations for Storing or Transmitting Data
Handling personal, financial, or health-related information brings a level of responsibility that goes beyond engineering. Once your product collects, processes, or transmits sensitive data; whether directly or through an AI service, you are accountable for protecting it.
Regulations such as the GDPR, CCPA, and HIPAA establish strict requirements. You're expected to secure data both while it's being transferred and while it's stored. This includes using encryption, limiting internal access, and having clear protocols in place for data handling and breach response. Depending on your use case, you may also need to reduce risk through anonymization, pseudonymization, or data minimization practices.
Encryption is critical. Any data that could identify an individual, such as names, email addresses, payment details, or medical information, should be encrypted during transmission and when stored. But encryption alone is not a complete solution. Compliance also requires well-defined retention rules, audit trails, and role-based access controls to prevent misuse or unauthorized exposure.
Using cloud infrastructure or external APIs does not remove these responsibilities. If customer data is routed through a third-party AI provider, your company is still responsible for ensuring that the transmission is secure and that the provider meets recognized security standards. A provider's claim that they don't retain your data doesn't absolve you of liability if something goes wrong along the way.
To manage this risk, companies should assess every vendor that interacts with regulated data. Ask for documentation on how the provider encrypts information, how long data is kept, how deletion requests are handled, and what certifications they maintain. Look for alignment with established frameworks such as SOC 2, ISO 27001, or NIST. If a provider can't demonstrate security readiness, consider it a red flag.
The stakes are high. A single incident can lead to regulatory scrutiny, loss of customer trust, or damage to your brand. Security isn't just a backend concern—it's foundational to building legally resilient AI products.
Who Owns the Outputs of AI Models?
For many AI companies, questions about ownership don't stop at training data or model weights—they extend to the outputs themselves. And the answer isn't always straightforward.
In most cases, ownership of AI-generated content depends on the rights associated with the inputs. If your model is trained on proprietary data from a third party, or if you're using a foundation model built by someone else, the outputs may be legally entangled. In some jurisdictions, those outputs could be treated as derivative works, especially if the training data or source model imposes licensing restrictions.
This is particularly relevant when working with commercial content or intellectual property. If your team generates marketing copy, product designs, or code using a third-party model, especially one trained on unknown or unlicensed data, you may face questions about whether you truly own the results.
It's not just a theoretical concern. Some foundation model providers impose limitations on what customers can do with the outputs. These may include restrictions on redistribution, resale, or use in downstream training. In other cases, providers reserve certain rights or require attribution.
To avoid confusion, make ownership part of your contracts from the start. If your company is building with external APIs or licensing model access, your agreement should explicitly state who owns the outputs, how they can be used, and whether there are any restrictions tied to commercialization or publication. This is especially important for companies building on top of generative platforms where the default terms may favor the provider.
For enterprise customers, clear output rights are becoming a standard ask. For startups, they're a smart way to build product defensibility and avoid future IP disputes. No matter where you are in the stack; model developer, integrator, or end user, ownership of outputs should be defined, not assumed.
Want to see how the conversation around AI and data ownership is unfolding in the public eye? This short CBS News segment highlights how tech companies are using personal data to train AI, and why privacy, consent, and transparency are now under the microscope.
Compliance Watch: Regulatory and Industry Trends
As AI technology evolves, so does the legal framework that surrounds it. Companies working with personal data, whether training models or integrating third-party APIs, need to stay aligned with both current laws and emerging standards. Here are the major regulations and practices shaping the future of AI compliance.
Major Legal Frameworks
GDPR (European Union)
The GDPR continues to set the global standard for data privacy. It
requires user consent before processing personal data, and it gives
users the right to access, transfer, or delete their information.
If your system processes data from EU residents, even indirectly,
you're responsible for how that data is stored, used, and
protected. This includes any personal information used in training
or passed through your AI tools.
CCPA / CPRA (California)
California's privacy laws give residents clear rights over
their data. Users can see what's being collected, opt out of
sharing, and request deletion. The CPRA adds further obligations,
including limits on profiling and requirements to minimize data
collection. These rules apply to any product serving California
users—whether or not the company is based in the state.
EU AI Act (Upcoming)
This new regulation will apply different requirements based on how
an AI system is used. High-risk systems, such as those in
healthcare, education, or employment, will need to meet strict
standards. Companies may need to document their training data,
explain how their models work, and provide human oversight for
certain decisions. Even general-purpose models may fall under the
Act's scope.
FTC Enforcement (United States)
The FTC is taking a closer look at how companies market AI products
and handle user data. Misleading claims—such as saying data
isn't stored when it is, or that AI is not used when it
actually is—can lead to enforcement. The agency has made it
clear that using data for training without user consent could
violate consumer protection laws.
Emerging Industry Standards
Model documentation and data transparency
Tools like "Datasheets for Datasets" and "Model
Cards" are becoming more common. These documents explain where
training data comes from, what assumptions the model makes, and how
it should be used. They're helpful for internal governance and
show regulators and customers that your company is building
responsibly.
Rising expectations from partners and
investors
Enterprise buyers and investors want more detail about your AI
systems. They may ask how models were trained, what kind of data
was used, and what steps were taken to secure it. Legal teams
should be ready to answer these questions during procurement,
partnerships, or fundraising.
Data Rights Are Product Infrastructure
When it comes to AI, data isn't just fuel. It's a legal asset, and sometimes a liability.
Whether you're building a model from scratch, using external APIs, or serving enterprise clients, data ownership and privacy are part of the product. They influence everything from customer trust to how fast you can scale. These issues can't be treated as technical afterthoughts. They need to be designed into the foundation.
Traverse Legal works with AI companies, SaaS teams, and data-driven startups to get ahead of these challenges. We help you protect your IP, navigate compliance, and build legal infrastructure that supports growth.
The content of this article is intended to provide a general guide to the subject matter. Specialist advice should be sought about your specific circumstances.
