The AI Gold Rush

We are living in a historic moment. Since March 2023, generative artificial intelligence has gone from a technological curiosity to an essential tool for millions of professionals. We all know that colleague who discovered ChatGPT and now can't live without it: they use it to draft emails, analyze data, generate creative ideas, and even write code. Productivity has skyrocketed, and the promise is irresistible.

But here is the question few people ask: Have you ever stopped to think about where that data goes and who is benefiting from it?

Every prompt you write, every document you paste, every question you ask, doesn't just generate a response. It also generates value... for someone else. And here comes the phrase that should keep you up at night:

"When an AI service is free or cheap, you are likely not the customer, but the product. Your confidential data is the payment."

Today I want to talk to you about the 5 hidden risks your company faces when using public AI. I'm not doing this to scare you, but so you can make informed decisions. Because AI is incredible, but only if you use it correctly.

The 5 Hidden Risks

Risk 1: You Are Training Your Competition (Literally!)

What it is: Many public AI platforms use your conversations, your prompts, and your data to re-train and improve their models. It's part of their business model: you give them valuable information, they improve the product.

Why it matters: Imagine you paste your new Q4 marketing plan into ChatGPT because you need to refine it. Or you upload snippets of your proprietary source code to debug it. Or you enter the confidential details of a multi-million dollar contract to analyze it.

What happens next? That information is now part of the model's "brain." It's embedded in its neural weights. And although companies claim the information is not directly reproducible, the reality is that your competitor could, with the right question and enough prompt engineering, extract similar insights or parallel strategies based on patterns the model learned from you.

The perfect analogy: It's like giving your secret recipe to a chef who works for every restaurant in the world.

Risk 2: Data Leaks and Bugs (The Samsung Case No One Forgot)

What it is: Public platforms, no matter how large and sophisticated, are not infallible. In March 2023, OpenAI acknowledged a critical bug that allowed some users to see titles of other users' conversations in their history. Although the bug was quickly patched, it revealed an uncomfortable truth: these platforms have vulnerabilities.

Why it matters: The Samsung case became a global case study. Employees in the semiconductor division pasted proprietary source code into ChatGPT to debug and optimize it. They also entered confidential meeting notes. What began as an attempt to be more productive ended in a corporate security crisis. Samsung banned the use of ChatGPT on its premises and had to invest millions in damage control.

The lesson is clear: human error plus a public platform equals an intellectual property disaster.

And it's not just Samsung. Companies of all sizes have faced similar incidents. The difference is that many never make them public.

Risk 3: The Legal Nightmare (Compliance and LFPDPPP/GDPR)

What it is: If an employee pastes a customer's personal data (an email, a tax ID, an account number, a medical history) into a public AI platform, where is that data stored? On a server in the United States? In Ireland? In a data center shared with other customers?

Why it matters: The answer is likely "yes" to all of the above, and that is a monumental legal problem.

In Mexico, the Federal Law on Protection of Personal Data Held by Private Parties (LFPDPPP) requires personal data to be processed with explicit consent and stored securely. In Europe, the General Data Protection Regulation (GDPR) is even stricter: fines can reach up to 4% of a company's global annual revenue or 20 million euros, whichever is greater.

When you use a public AI platform, you lose control over data jurisdiction. And if a breach occurs, the company is liable, not the AI provider.

The real-world scenario: A sales executive pastes the potential customer database into a public AI to segment it. A week later, the legal department receives a notification from INAI or a European authority. The fine can be devastating, but the reputational damage is irreparable. Your company goes from being a trusted partner to an example of privacy negligence.

Risk 4: Dependency and Zero Sovereignty

What it is: You build your company's critical processes on a third-party API. It could be for customer service, financial analysis, content generation, or report automation. Your operation depends on that service working 24/7/365.

Why it matters: What happens if the provider decides to raise the price by 1000% tomorrow? (It has already happened with the Twitter/X API.) What if they change the terms of service and restrict key functionalities? What if the API goes down for hours, right on your fiscal year-end day?

The answer is simple: your company is paralyzed. You have no control. You have no alternatives. And you don't have a Plan B because your entire infrastructure is built on sand.

The irony: Companies that quickly adopt public AI to "be more agile" end up being the most rigid and vulnerable.

Risk 5: Generic, Not Expert, Answers

What it is: Public models are trained on "the entire internet." They are generalists by design. They know a little about everything, but are experts in nothing.

Why it matters: Your business is unique. You have internal manuals, specific processes, a company culture, customers with particular needs, support ticket histories, and financial and operational data that no one else has.

A public model knows none of that. When you ask it about your manufacturing process, it gives you a generic answer based on "best practices." When you ask it to analyze your finances, it has no context about your industry, your seasonality, or your KPIs.

The value you get is superficial. It's like hiring a consultant who only read the title of your business case.

The Solution: Private AI – The Only Enterprise Architecture

This is where I need to be clear: the solution is not to stop using AI. The solution is to use it correctly.

At Leeuwwolk, our thesis is simple: if AI is going to be a strategic asset for your company (and it should be), then it must be yours. Not OpenAI's. Not Google's. Yours.

That is what we call Private AI: an artificial intelligence ecosystem that gives you total sovereignty.

What does Private AI mean?

1. Controlled Infrastructure: The language models (LLMs) run on your servers (on-premise) or in our dedicated private cloud that we manage for you. There are no intermediaries. No shared APIs. Latency is near zero, and access is controlled by your firewall.

2. Secure Data: Your data never leaves your perimeter. We encrypt it with AES-256 (the military standard) and connect it with RAG (Retrieval-Augmented Generation) techniques using vector databases like Qdrant. The model responds only with your information, extracted in real-time from your systems.

3. Total Control: It's your asset. You don't depend on price changes, terms of service, or external availability. You have the code. You have the data. You have the model.

Conclusion: Don't Sacrifice Security for Innovation

AI is the biggest technological shift we've seen since the internet. But like the internet in its early days, it's fraught with risks for those who don't take precautions.

At Leeuwwolk, we believe you can have innovation and security. You can have speed and control. You can have the power of generative AI without risking your intellectual property, your legal compliance, or your strategic independence.

The question is: Are you willing to build your competitive advantage on someone else's infrastructure, or do you prefer to have sovereignty?

Are you ready to discover the AI security "blind spots" in your company?

Schedule a Free AI Diagnosis with us. In a 60-minute session, we will analyze:

  • Where your team is using public AI (even if you don't know it)

  • What confidential data is at risk

  • How to design a Private AI architecture specific to your industry