Let’s imagine a typical marketing agency or law firm in the city center. Time pressure is high, deadlines are looming. A junior specialist, let’s call him Michael, gets a task: review a complex NDA agreement with a new, key client. The document is 15 pages of dense legal text.
Michael thinks: “Why waste an hour? The AI will do this in 30 seconds.”
This is the moment where – in our scenario – a nightmare for company security begins.
”I’ll Just Quickly Check…”
In our example, the employee copies the entire content of the confidential agreement into a public chat with the question: “Are there any unusual clauses in this NDA that I should pay attention to?”
The AI responds instantly, pointing out risks. The employee is satisfied – he saved time.
However, he doesn’t realize a key issue: the full content of the agreement has just landed on external servers, and depending on account settings, it could be included in the model’s training database.
What Did the Pasted Document Contain?
Documents of this type usually contain:
- Names of both parties – revealing who the company is doing business with
- Project details – e.g., the name of a new drug or product before launch
- Personal data – names of board members, email addresses
- Financial clauses – rates and contractual penalties
All this information, according to the confidentiality clause, should never leave the company’s secure infrastructure.
The Leak Mechanism: How AI “Learns” Secrets
When using free or standard versions of public AI models, you often accept terms that allow the provider to use your conversations to “improve services.”
In our hypothetical scenario, after some time, the AI model – “trained” on data from Michael’s agreement – might start using this information. Another user, asking for example about “standard penalty rates in industry X”, could receive an answer based on your company’s confidential data.
Consequences: A Catastrophic Scenario
If such a leak came to light, the company would face serious problems:
1. NDA Breach
The client could demand massive compensation for breach of confidentiality. The mere fact that information about the cooperation reached a competitor could be enough.
2. GDPR Proceedings
Pasting personal data (signatures, names) into a tool without a Data Processing Agreement (DPA) is a direct path to a penalty for GDPR violation.
3. Reputation Loss
In trust-based industries (law, finance, medicine), news that a company is “feeding” client data to public AI could mean the end of the business.
How to Avoid This Scenario?
Most employees don’t have bad intentions – they just want to work faster. Michael’s mistake was using the wrong tool.
❌ Wrong Approach:
Pasting documents with sensitive data into public, free chatbots.
✅ Correct Approach:
The company should provide a secure working environment:
- Implement the PrivatAI.pl private model, which runs locally or in a private cloud. In this model, data is analyzed but never used for training and never leaves the defined infrastructure.
- Anonymization – if you must use a public tool, always remove company names, amounts, and personal data.
- Education – employees must know that the public chat window is not a notepad, but an external cloud service.
Warning Signs: What NOT to Paste into Public AI?
Never process in the public cloud:
🚨 Documents with confidentiality clauses (NDA) 🚨 Client databases and personal data (GDPR) 🚨 Business strategies and marketing plans before launch 🚨 Financial results before publication 🚨 Proprietary source code
Summary
The story described is a hypothetical scenario, but the risk is very real. Companies like Samsung, Apple, and Amazon have long restricted their employees’ access to public AI tools for this exact reason.
Don’t wait for this scenario to happen in your company.
Secure your data by implementing the PrivatAI.pl solution – a system that gives you the power of artificial intelligence but keeps data under your full control, on Polish/EU servers.
Check how to safely implement AI in your company
Note: The article above is a case study illustrating potential threats associated with the improper use of public language models. All names and situations are exemplary.