Public AI Tools Are Already in Your Workplace
Whether you like it or not, generative AI tools like ChatGPT, Gemini, Grok, and Claude have already entered your workplace. Employees across departments are using them to
- Draft emails with confidential data
- Creating internal product roadmaps or feature specs for feedback
- Summarize documents and draft internal documentation
- Make Minutes of the critical meetings (MOM)
- Make presentations
- Generating or refining legal contracts using real client data
- Data analysts feed raw client datasets to generate insights
- Feeding confidential emails or chat logs for drafting responses
- Uploading designs or mockups for UI feedback
- Debug proprietary codes, and more.
Why? Because Large Langauge Models are fast, smart, and available at the click of a button.
But here’s the danger: these tools don’t just answer questions—they learn from every prompt. And what they learn could include your proprietary business data.
Gartner predicts that by 2026, 40% of large enterprises will be running private LLMs—up from less than 5% in 2023. This isn’t just a tech trend; it’s a tectonic shift in enterprise data ownership and AI strategy.
Companies like McKinsey & Company Gartner , and Forrester have all highlighted the growing urgency for data-secure, domain-specific LLMs—especially in regulated sectors like finance, healthcare, and law.
Public AI Tools (like GPT) Are Unstoppable—And That Should Scare You
Let’s rewind.
Remember when smartphones first came into offices? Companies tried banning them at the workplace out of fear. They worried about distractions, data theft, and misuse. Then came the internet—admins blocked websites, firewalls were deployed, and access to the internet was tightly controlled.
But eventually, the truth became clear: these technologies weren’t threats but necessities.
The same pattern is repeating with public AI (GPT).
Employees use them because they boost performance. You can try to stop them, but history shows that’s a battle you’ll lose.
The smart move? Don’t ban them.
Replace them with safer alternatives.
What’s the Risk? Just Ask Samsung
In April 2023, Samsung engineers pasted internal code into ChatGPT. That data potentially became part of the model’s training set forever. The company swiftly banned generative AI tools across departments. But the leak had already happened.
Let’s make it more relatable:
Imagine you’re a world-famous baker. Your signature cake recipe is a closely guarded family secret passed down through generations. One day, your cousin feeds ChatGPT a prompt:
“What if I mix almond flour with saffron soaked in rose water and a pinch of cardamom?”
Boom. Your secret’s out.
Now, someone across the globe asks ChatGPT, “How can I make a luxurious sponge cake?” And it may respond with your recipe’s core idea.
You didn’t sell your secret. You gave it away. That’s what happens when your employees use public LLMs without guardrails.
Thought leaders like Andrew Ng and Allie K. Miller have long emphasized that true enterprise value in AI comes not just from innovation—but from integration. You don’t need a 175B model. You need a model that understands your data, your workflows, and your compliance needs.
Why Private AI Tools (LLMs) Are a Game-Changer for Your Business
Unlike public AI tools (ChatGPT, Gemini), private LLMs run securely on your infrastructure or a dedicated virtual private cloud. Your data never leaves your environment—you retain complete control over the model, data, and insights.
But it’s not just about security. It’s about powerful customization.
Key Benefits of a Private LLM:
- Industry-Specific Training Build a truly intelligent assistant by fine-tuning the LLM with your own documents, knowledge base, workflows, and processes.
- Ask Complex, Contextual Questions Like:“What’s the most updated SOP for onboarding clients in our logistics vertical?” “Summarize all client feedback on Product X from the last 6 months.” “What were the top compliance risks flagged in our financial audits last year?” “Show me the top-selling products of the last six months.”
- Access Control at Every Level Decide who can ask what—the finance team sees financial queries, HR accesses HR insights, sales reps get sales support—all from the same private brain.
- Full Data Sovereignty Your data stays in your hands: no third-party access, no training on your inputs.
- Enhanced Security No risk of data leaks, GDPR violations, or unintended model behavior.
- IP Ownership Your trained model becomes a proprietary asset—a competitive edge over your competitor that no one else can replicate.
- Dedicated Infrastructure = Peak Performance No shared servers. No slowdowns. Always ready to serve your teams.
Private AI vs. Public AI: Side-by-Side Comparison
Private LLMs offer not only control and security but also substantial long-term value—something we’ll explore in depth in a separate post
Hybrid Option: Best of Both Worlds
You don’t have to go fully private to gain more control. Many enterprises use hosted LLM services through trusted cloud environments. For example, you can deploy GPT models via Azure’s OpenAI Service or access Claude via Anthropic’s Amazon Bedrock integration.
These hybrid options offer faster time-to-deployment, better control over data flow, and enterprise-level compliance, without requiring you to manage the model infrastructure fully.
Open-source initiatives led by companies like Hugging Face and Stability AI (founded by Emad Mostaque) have made it easier for enterprises to explore private deployments without locking themselves into a single vendor.
At the same time, cloud giants like AWS , Microsoft Azure , and Google Cloud are offering hybrid models—but enterprises must still build the orchestration and guardrails themselves.
Final Word: The Longer You Wait, The Harder It Gets
While you’re debating, your competitors are:
- Automating workflows
- Accelerating decision-making
- Protecting their data
- Training models on their domain
Once they gain this edge, catching up becomes expensive—or impossible.
Private LLMs are not optional anymore. They’re the only way forward.
If you think you can delay this decision, you’re mistaken. Just as companies once resisted smartphones and internet access in the workplace—and eventually had no choice—private LLMs are becoming non-negotiable.
Publications like TechCrunch , WIRED , and CBInsight.com have consistently documented how uncontrolled usage of public LLMs like ChatGPT and others has already led to data leakage and shadow AI inside enterprises.
Transform Your Organization with Responsible AI
At Matellio, we’ve had the privilege of working with forward-thinking enterprises to not only implement private LLMs, but to build sustainable AI cultures that drive business value and innovation. We’re not just a service provider—we’re a partner in transforming your AI vision into reality.
Here’s how Matellio can help:
- Choose the right model architecture (small vs. large, open-source vs. commercial)
- Optimize data usage for cost and performance
- Determine when to use lightweight models and when to scale up
- Define effective training checkpoints
- Prepare and structure training datasets for high-quality fine-tuning
Whether you’re exploring a hybrid setup or designing a long-term enterprise-grade AI strategy, Matellio brings deep expertise across industries, tools, and technologies to guide your journey.
If you’re just starting out and want a casual conversation about possibilities, or if you already have something in place and want honest feedback to improve—it would be a pleasure to talk.
We always look forward to interesting conversations around AI transformation. Feel free to schedule a call or reach out to us at info@matellio.com
