If you're using generative AI tools, have you paused to consider how your data is treated? For many users, it’s easy to overlook this detail, until sensitive business knowledge accidentally finds its way outside the company. The reality is, if you’re not paying very close attention to how your vendor processes your prompts, your confidential data risks being used as next week’s “training example”. Or, even worse, finding its way into your competitor’s playbook.

This guide takes a direct look at why customer data should not be used for training, how Reliant draws distinct boundaries between your usage and our AI models, and the real trade-offs to think through before sharing your intellectual property with any large language model (LLM) provider. Treat this as a friendly PSA from your (security-conscious) neighborhood AI team.

The Not-So-Fine Print: Your Data Is Training Someone’s AI

Unless your company has a locked-down enterprise contract with OpenAI, every query you make to ChatGPT is stored and leveraged to train future models. This is specified clearly in their policy. The story is similar on platforms like Slack, where your messages serve dual duty as AI training fodder unless your admin has explicitly opted out.

For industries like pharma, biotech, or really any sector where proprietary expertise is the edge that matters, this should raise alarm bells. Here’s why.

Why Training on Customer Data is Risky
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1. Extractable Memorization Attacks

Remember the DeepMind paper that made the rounds in late 2023? The team there demonstrated that with the right techniques, you can prompt an LLM to regurgitate pieces of its training data verbatim (DeepMind, 2023). You don’t need to work for an intelligence agency, just some persistence and clever prompting skills. Companies like Samsung got so spooked, they banned ChatGPT on employee devices. Not exactly a ringing endorsement for “don’t worry, it’s just machine learning!”

2. Accidental Disclosure of Your “Secret Sauce”

AI doesn’t have to reproduce your data word-for-word to leak value. The unique ideas, insights, or project nicknames you feed an LLM can become embedded as patterns, ready to pop up in future outputs. Maybe you’re the first in your space to correlate a gene variant with a disease. Six months later, your competitor is seeing that innovation as a suggestion. Not ideal.

3. Data Privacy Minefields

Using customer data to train models creates a larger data haystack, with plenty of opportunities for things to go wrong. Tightly mixing business-sensitive information makes leaks, regulatory headaches, and blame shifting much more likely. If you’d rather not have that conversation, rigorous separation is the path forward.

4. Confusing “Live” with “Learned”

There’s another layer to this issue as well. When models are trained on historical data but then presented with current context, it’s easy to blur the lines. Are you getting a response based on today’s reality, or an outdated summary from last year? For researchers working on rapidly evolving projects, this inflexibility is more than inconvenient; it can be outright risky.

5. LLMS are Valuable, But...

The upside to LLMs is huge, especially in data-rich fields craving actionable insight. Used wisely, they’re capable of producing breakthrough results. But getting value shouldn’t mean having to gamble on privacy. You can have “future-proofed” business intelligence without tossing your hard-earned competitive advantage into a community pot.

Reliant’s Approach to Data Security

At Reliant, trust is non-negotiable. Here’s how we approach it:

• Clear Separation: We keep model training and user activity entirely separate. Model development draws from relevant domain expertise and best practices, but never from your company’s proprietary data, projects, or patient files.
• On-the-Fly Insight Extraction: Our models extract insight by analyzing your real-time context only when you use them. Answers are anchored in current, live data—not a fixed, aging training set. When something changes in your organization, our AI adapts instantly.
• Learning Safely: We do use user interaction data to improve extractive performance, but strictly to see if (for example) the model successfully flagged the enzyme named in your uploaded paper. This process never “cross-pollinates” sensitive information between users or across organizations.
• Private Knowledge Graphs: Your uploads build a private knowledge graph, deployed in a dedicated cloud instance and only accessible to you. This is your AI’s private, airtight “library”. No risk of leaks, no mixing with the broader internet.

What You Should Really Care About

There are many ways to train and improve AI models. None are inherently wrong, but every method carries trade-offs. The key question is “What am I giving up to get value?” Sharing generic FAQs? Minimal risk. Sharing cutting-edge internal methodology? Maybe not worth the upside. Assess your real risks and appetite before clicking “accept” on new terms of service.

The Path Forward for Security-Minded Organizations

If you made it this far, you now know enough to be dangerous (to the status quo, at least). At Reliant, we’re committed to making technical excellence and data stewardship work hand-in-hand. If you’re interested in exploring how Reliant can support a higher standard of data privacy while delivering real-time, actionable insight, get in touch. Trust isn’t just a slogan for us; it’s the product. And we’re ready to prove it.

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