The Golden Rule: Treat AI Like a Public Forum
The most important principle to remember is that many publicly available generative AI tools use the data you provide to train their models. Think of it this way: if you wouldn’t be comfortable posting the information on a public website or shouting it in a crowded
room, don't paste it into a public AI prompt. Once information is entered, you lose control over it. It could potentially be reviewed by the AI provider's staff or even surface in a response to another user's query somewhere down the line. This is why many companies are developing strict policies and providing training on acceptable AI use. Some even offer enterprise-grade AI tools that operate in a closed system, preventing data from being used for public model training. Always check which tools your company has approved.
Client and Customer Data
This is a major red flag. Inputting any information that belongs to your clients or customers is a serious breach of confidentiality and trust. This includes names, contact details, contract specifics, project data, or any communication you've had with them. Leaking this type of third-party data not only damages your company's reputation but can also lead to significant legal and financial penalties. Your clients trust you to safeguard their information, and that responsibility extends to how you use productivity tools. Using an AI to summarize client meeting notes might seem efficient, but if those notes contain sensitive details, the risk far outweighs the reward.
Confidential Company Information and Trade Secrets
Every company has information that gives it a competitive edge. This includes unreleased financial reports, sales data, marketing strategies, product roadmaps, merger and acquisition plans, and internal audit results. In 2023, engineers at Samsung accidentally leaked confidential source code and internal meeting notes by pasting them into ChatGPT. This incident served as a wake-up call for companies worldwide, highlighting how easily sensitive intellectual property can be exposed. Feeding this kind of proprietary data into a public AI is like handing your playbook to the competition. The convenience of getting a quick summary of a financial document is not worth the risk of compromising your company's strategic position.
Personally Identifiable Information (PII)
Personally Identifiable Information, or PII, is any data that can be used to identify a specific individual. This includes employee records, health information, home addresses, phone numbers, and financial details like bank account or social security numbers. Sharing this information via a public AI tool is a significant privacy violation and security risk. Large Language Models can and do memorize parts of their training data, and researchers have successfully extracted PII from them. Even if the AI tool's terms of service promise privacy, the risk of a data breach or an accidental leak remains. If it's information you wouldn't want attached to your own name publicly, don't input it for anyone else.
Proprietary Code and Internal IT Data
For developers and engineers, it can be tempting to paste a block of troublesome code into an AI to find a bug or generate a solution. However, this code is often valuable intellectual property. Leaking it could expose vulnerabilities that hackers could exploit or give away a unique process your company developed. The same goes for internal IT documentation, network configurations, and security protocols. Sharing any of this information creates a potential roadmap for cybercriminals. Instead of public AI tools, use secure, internally-approved development environments and collaboration platforms for debugging and problem-solving.
Internal Deliberations and Personal Opinions
Generative AI should not be your digital diary or a place to vent about a project or colleague. Avoid pasting in emails, direct messages, or performance review drafts. While it might seem harmless to ask an AI to help you phrase a difficult email, these communications are part of your company's internal record. Furthermore, using AI to navigate sensitive interpersonal or leadership challenges is risky. AI models don't understand nuance, context, or workplace politics. Rely on human judgment, mentorship, and your company's official HR channels for these complex situations.
