Protect Your Competitive Edge, Your Clients, and Your Company
As artificial intelligence (AI) revolutionizes the way we work, it’s easy to get swept up in the convenience of public tools like ChatGPT, Google Gemini, and Claude. While these platforms offer incredible capabilities, they also come with real risks—especially when it comes to proprietary or sensitive business data. As a senior solutions engineer manager, I see the tension firsthand: the desire to innovate versus the need to protect what makes a business unique.
The Temptation: AI-Powered Efficiency
Public AI tools are remarkably powerful. In seconds, they can generate reports, summarize documents, draft emails, and write code. It’s tempting to paste internal data into these tools for faster results—and many users do it without thinking twice. But here’s the reality: Sharing proprietary data with a public AI model is kind of like leaving your company’s secrets out on the breakroom table—anyone could walk by and grab them. If you wouldn’t want to post it on your company’s website, you shouldn’t put it into a public AI model.
What Happens When You Put Data into a Public AI?
While major providers claim not to store or use your data for training (unless opted in), data leakage is still a risk—especially if you don’t fully understand the tool’s terms of service. Some models may retain snippets of input temporarily to improve performance or ensure quality. And if the platform is compromised or misconfigured, there’s no guarantee your data won’t end up in the wrong hands.
Key Risks
- Intellectual Property Exposure: Designs, strategies, codebases, or client deliverables may unintentionally be retained and surface in other outputs.
- Client Confidentiality Violations: Sharing customer data could breach non-disclosure agreements (NDAs), contracts, or compliance regulations.
- Data Sovereignty Issues: Public models may transmit data across borders, violating industry-specific or regional privacy laws.
- Lack of Audit Trails: Most public models don’t allow full traceability or access logs, which limits accountability.
Proprietary Data = Competitive Advantage
Your proprietary data—from internal processes to client insights—is your competitive edge. Once that information is out in the open, even unintentionally, it’s no longer proprietary. And once it’s part of a model’s training data (in some cases), it can’t be undone.
The Safer Alternative: Private, Enterprise-Grade AI
To harness the benefits of AI without sacrificing security, companies should look to:
- Private deployments of language models, such as hosted versions of ChatGPT, Llama, or Claude in isolated cloud environments.
- On-premise or virtual private instances that keep all data behind your firewall.
- Fine-tuned models with company-specific data, where access controls and governance are built in.
These solutions offer encryption, identity-based access, data residency control, and full audit logging—all essential for enterprise compliance.
Best Practices to Adopt Now
- Establish a clear AI usage policy—and train employees on what data is off-limits.
- Review the terms of any AI tool before use—especially regarding data usage and retention.
- Use private AI solutions for any business-critical tasks.
- Implement data classification so employees understand what qualifies as “proprietary.”
- Involve IT, legal, and security teams in any AI adoption discussions.
AI is transforming business—but the transformation must be secure. As technology leaders, it’s our responsibility to enable innovation while safeguarding the data that defines our businesses. Public AI tools are powerful assistants, but when it comes to proprietary data, discretion is non-negotiable.
At K3 Technology, we build customized, private AI solutions that empower smarter work and better decision-making. Want to explore what AI can do for your business? Let's talk!
