A Leader's Guide to AI Privacy: Who's Learning from Your Company's Data?

When an employee uses an AI like ChatGPT, it's not just a two-way conversation. A third party—the AI provider—is in the room. What your team shares, the AI learns. And what the AI learns can have major consequences for your business.

This guide is for business leaders. It cuts through the jargon to explain, in simple terms, the risks and rewards of using AI, so you can build a common-sense framework to protect your company's most valuable assets.

The Billion-Dollar Question: Is Your Secret Sauce Still a Secret?

The single most important thing to understand is this: AI models are designed to learn from the data they process.

If your team uses a public or consumer-grade AI to draft a patent application, analyze a confidential M&A target's financials, or write code for a proprietary algorithm, that information doesn't just vanish. It can be absorbed by the model, potentially influencing future responses for other users—including your competitors.

The risk isn't just about trade secrets. It's about all sensitive data:

• Intellectual Property: Unannounced product designs, source code, research.
• Business Strategy: Marketing plans, internal reports, M&A discussions.
• Customer and Employee Data: Information protected by regulations like GDPR.
• Legal Documents: Privileged information from contracts or litigation.

The Critical Difference: Public AI vs. Business AI

Not all AI services are created equal. The single most important distinction a business leader can make is between a free, public tool and a paid, enterprise-grade service.

• Public/Consumer AI (The Public Park): Using a free AI for work is like discussing a confidential deal on a park bench. It's easy and accessible, but you have no control over who might overhear you. Your data is almost certainly being used to train the model.
• Enterprise/Business AI (The Secure Boardroom): A paid, enterprise plan is a private, contractual agreement. The AI provider gives you a secure environment and legally commits not to train their models on your data.

A Look at the Major Players: Policies for Businesses

Here’s a simple breakdown of the leading AI providers' policies for business users:

• OpenAI (ChatGPT): Their paid Enterprise plans are secure and private; OpenAI commits not to train on your data (Source). However, the free version does train on user inputs.
• Google (Gemini): When used within a paid Google Workspace account, your data is protected by your Workspace agreement. The free, public version of Gemini does use your conversations for training (Source).
• Anthropic (Claude): Widely seen as a privacy-focused option. Their business plans do not train on customer data by default, and this is a core part of their "Constitutional AI" safety approach (Source).
• DeepSeek: A powerful model, but its privacy policy states that data is stored and processed in China. For most businesses outside of China, this presents significant legal and security risks due to different data sovereignty laws (Source).

A Word of Warning: "Deleted" Doesn't Always Mean Gone

The ongoing lawsuit between The New York Times and OpenAI reveals a critical point. A U.S. court ordered OpenAI to preserve all user data, even if a user deleted it. This shows that legal obligations in one country can override an AI company's privacy policy and a user's right to be forgotten—a crucial consideration for global businesses (Source).

Taking Control: On-Premise and Private AI

For organizations with maximum security needs (e.g., defense, finance, healthcare), there's a third option beyond the public park or the provider's boardroom: building your own.

Companies can now run powerful AI models on-premise (on their own servers) or in a private cloud. This gives you complete control. Your data never leaves your environment, and you are not sharing it with any AI provider.

• How it works: Tools like Ollama and platforms from Hugging Face make it possible to deploy powerful open-source models (like Llama 3 or Mistral) inside your own firewall.
• The Trade-off: This approach offers the ultimate security but requires significant technical expertise and hardware investment (primarily powerful GPUs).

Your Company's AI Playbook: Common Sense Rules

Most data leaks aren't malicious; they're accidental. The best defense is a clear, simple policy that every employee understands. Many enterprises are now implementing such policies.

Your role as a leader is to ensure your teams know the rules of the road. If you don't have a policy, now is the time to create one.

Key Principles for Your AI Policy:

1. Know Your Data: Classify information. Is it public, internal, confidential, or a trade secret?
2. Use the Right Tool:
   • Confidential/Secret Data: Should only be used in the company-approved Enterprise AI service or an on-premise model.
   • Public/Non-Sensitive Data: Free tools may be acceptable.
3. When in Doubt, Ask: Employees must know who to consult—usually the IT, Security, or Legal department. These teams are not there to say "no," but to show you how to innovate safely.

Before using any new AI tool for work, every employee should be able to answer: "Does our company policy permit me to put this specific type of information into this specific AI service?"

Final Recommendations for Leaders

1. Treat AI as a Vendor: An AI is not just software; it's a third party you are entrusting with data. Evaluate it with the same rigor you'd use for any new partner.
2. Invest in Enterprise-Grade AI: If your team needs AI, provide them with a secure, company-approved enterprise version. It's a critical investment in data security.
3. Establish a Clear AI Policy: Don't wait for an incident. Work with your CISO and legal counsel to create and communicate a simple, actionable AI usage policy.
4. Educate Your Team: The biggest risk is a lack of awareness. Train your employees on the "why" behind your AI policy to build a culture of security-conscious innovation.