Key Takeaways
- If a vendor trains on your data, your proprietary information could benefit competitors using the same tool
- Training on customer data creates risks: confidentiality erosion, compliance complications, competitive leakage, and loss of control
- "No training on customer data" should be a baseline requirement, not a premium feature
- Get commitments in writing, in the contract—and verify they cover the entire technology stack
When you're evaluating AI vendors, you'll hear a lot about features. The capabilities, the integrations, the interface, the roadmap. All important things.
But before you get into any of that, there's a more fundamental question: what happens to your data? Specifically, does the vendor use your data to train their AI models?
This sounds technical, but the implications are straightforward. If a vendor trains on your data, the information you put into the system doesn't just get processed and forgotten. It becomes part of the model itself—potentially influencing responses for other customers, persisting in ways you can't control or delete, and blurring the line between your proprietary information and the vendor's product.
This should be disqualifying. And increasingly, sophisticated buyers are treating it that way.
What "training on your data" actually means
AI models learn from data. The more data they see, the better they get at recognizing patterns and generating useful outputs. This creates a powerful incentive for AI vendors: every piece of data customers put into the system is potential training material.
When a vendor trains on your data, your inputs—the questions you ask, the documents you upload, the information you share—get incorporated into the model's knowledge. The model learns from your data and applies that learning when responding to everyone, not just you.
This might seem harmless. Maybe even beneficial—don't you want the model to be smarter? But consider what you're actually giving away.
Your proprietary processes and procedures. The internal documents you uploaded are searchable. The questions your employees ask, which reveal what they're working on and what they don't know. The patterns of your business are embedded in how you use the tool. All of this becomes part of a model that serves your competitors, too.
The problems are real
This isn't a theoretical risk. There are concrete issues with vendors' training on your data.
Confidentiality erosion. Information you consider confidential becomes part of a shared model. Even if it's not regurgitated verbatim, it influences responses in ways you can't see or control. Your trade secrets, your strategies, your internal discussions—absorbed into a system that serves thousands of other organizations. This is especially concerning for sensitive HR data and other confidential information.
Compliance complications. Many regulatory frameworks require you to control what happens to sensitive data. Establishing clear AI governance policies becomes much harder when data flows outside your environment. GDPR gives data subjects rights over their information—including deletion. If their data has been used to train a model, can you actually fulfill a deletion request? The honest answer is often no.
Competitive leakage. The AI you're using to gain a competitive advantage is simultaneously learning from all of your competitors who use the same tool. The collective intelligence includes everyone's proprietary information. You're all making each other smarter—and the vendor is the real beneficiary.
Lack of control. Once data is used for training, you can't take it back. You can stop using the service, but the model has already learned from your inputs. There's no "untraining" that removes your contribution.
The irreversibility factor
Once your data is ingested into the weights and parameters of a neural network, extracting it is technically nearly impossible. Unlike a database where you can delete a row, an AI model "remembers" concepts and patterns diffusely. This means once you consent to training, you effectively lose the ability to recall that data later. This irreversibility makes the initial decision to allow training a critical point of no return.
Why do vendors do it anyway?
Training on customer data is valuable to AI vendors. It makes their models better without them having to pay for training data. Every customer becomes an unpaid contributor to their product development.
Some vendors are transparent about this. They explain that data improves the model and frame it as a benefit—"you're helping make the AI smarter for everyone." Others bury it in terms of service that nobody reads. You might be training their model right now without realizing it.
Some offer opt-outs, but the default is training. You have to know how to ask, and then you have to hope the opt-out is actually honored. The incentive structure is clear: using your data benefits the vendor, and most customers don't know how to object. So the practice continues.
What does "we don't train on your data" mean?
When a vendor commits to not training on your data, it means:
- Your inputs remain your inputs. They're processed to give you a grounded, accurate response, but they don't become part of the model. They don't influence what the model says to other customers. They stay within the scope of serving you.
- You retain control. Your data can be deleted when you delete it. It doesn't persist in a form you can't reach. When you stop using the service, your data stops being relevant to the service.
- Confidentiality is preserved. Your proprietary information stays proprietary. It's not absorbed into a shared resource that serves everyone, including your competitors.
- Compliance is simpler. When data subjects have rights over their information, you can actually fulfill those rights. You're not in the awkward position of promising deletion while knowing the data has already been baked into a model.
This should be the default
A few years ago, training on customer data was common, and few buyers thought to question it. The technology was new, the implications weren't widely understood, and the excitement about AI capabilities overshadowed concerns about data practices.
"We don't train on your data" is becoming a minimum requirement—not a feature to brag about, but the baseline expectation.
That's changing. Organizations are learning the hard way about the risks of unclear data practices. Regulators are paying attention. Sophisticated buyers are asking hard questions.
The vendors who don't train on customer data are increasingly winning deals that the other vendors lose. Not because of features or price, but because of trust. Because the buyer's legal team, security team, or executive team said "we can't accept these data practices."
This is becoming table stakes—a minimum requirement that every serious vendor should meet. Not a feature to brag about, not a premium offering, but the baseline expectation. If a vendor can't commit clearly to not training on your data, that should be a disqualifying factor. There are too many options in the market that will make this commitment for you to accept one that won't.
How to verify
Vendors know that "we don't train on your data" is what buyers want to hear. Some will say it without meaning it, or with carve-outs that undermine the promise. Here's how to verify you're getting a real commitment.
Get it in writing, in the contract. Terms of service can change. Verbal assurances are worthless. A contractual commitment that the vendor will not use your data for model training is the only thing that counts.
Ask about third-party models. Many AI tools use underlying models from providers like OpenAI, Anthropic, Google, or others. Even if the vendor doesn't train on your data, what about the model provider? Make sure the commitment covers the entire stack.
Ask about exceptions. "We don't train on your data except for..." is not a commitment. Understand what, if any, exceptions exist. Aggregated usage statistics might be reasonable. Using your actual content for training is not.
Ask about the default versus the opt-out. If you have to opt out, and the default is training, you're depending on having asked the right question at the right time. The default should be no training.
Check for consistency
If the vendor's marketing says one thing and their terms of service say another, believe the terms of service. That's what's legally binding. Marketing materials are often written by teams disconnected from the legal reality of the product. Scrutinize the fine print in the Data Processing Addendum (DPA) to ensure it aligns perfectly with the sales pitch.
The market is moving
Enterprise buyers increasingly require clear data commitments before they'll consider an AI vendor. Security questionnaires specifically ask about training practices. Procurement processes screen for this early.
Vendors who train on customer data will find themselves excluded from deals they used to win. The ones who don't train on customer data will win on trust, even if their features aren't quite as flashy.
If you're evaluating AI vendors, make this one of your first questions, not as a nice-to-have, but as a requirement. This applies across all use cases, including AI for HR and other enterprise applications. The vendors who meet this bar are the ones who deserve your business.
If you're an AI vendor still training on customer data, the writing is on the wall. This practice is becoming unacceptable to the buyers you want to serve. The sooner you stop, the better positioned you'll be.
"We don't train on your data" should be table stakes. It's time to make it so.
JoySuite doesn't train on your data. Period. Your information stays yours—used to serve you, not to build our models. That's not a premium feature. It's how we operate.
