There’s something a little unsettling about watching an employee drop a client brief into ChatGPT. The employee is probably trying to save time due to a tight deadline, a quick rewrite, or a clean summary. But the moment that file leaves your ecosystem, your company’s reputation might go with it.
Cyberhaven’s recent data shows roughly one in ten (11%) inputs to ChatGPT include confidential material. That confidential material could be HR records, financials, or entire proposals meant for your client’s eyes only. If even a single snippet resurfaces elsewhere, it could lead to a contractual breach.
How can you prevent this problem without stifling innovation? Let’s talk about how a living, breathing internal AI policy, one that evolves with your people, can keep creativity flowing and data where it belongs.
When ChatGPT launched, it felt like a shortcut to brilliance. Write faster, think faster, and ship faster. Then people began to realize that the “send” button also sends data outside your network.
That’s the quiet danger, employees use AI tools not out of carelessness but for efficiency, trying to do good work. Yet tools like ChatGPT blur the line between public and private computing. Unless your employees are using an enterprise-grade version, whatever goes in may stay there.
The Verizon 2024 Data Breach Report found that human error was still among the leading causes of compromise. Credentials get reused, access permissions stretch too far, and internal data ends up exactly where it shouldn’t.
An AI policy isn’t about micromanagement, it’s about giving everyone a clear map and visible boundaries to work within.
Every company needs a rulebook. However, the difference between a policy that people follow and one that they ignore comes down to tone and clarity. Here’s how to make yours work.
You don’t have to invent your own ethics code from scratch. The NIST AI Risk Management Framework as well as CISA’s AI Security Guidelines already outline what responsible AI use should look like: governance, measurement, accountability, and ongoing review.
Using these standards as your framework saves time and lends credibility to your policy if regulators or clients ever question how you’re protecting their information.
Ask ten people what “confidential data” means and you’ll get ten answers. That’s why vague instructions like “don’t share anything private” rarely stick. Spell it out:
Once those tiers are in place, employees will understand the boundaries before they type a single word into a prompt. Pair this framework with a data-handling guide, so they can anonymize examples instead of risking exposure.
If your team uses AI, make sure it’s the enterprise version. Platforms like ChatGPT Enterprise or zero-retention APIs allow you to disable data training and control retention.
OpenAI has recently noted that some user data might still be retained for legal reasons in non-enterprise tiers. That’s reason enough to restrict access to approved systems only.
Keep an AI Register of sanctioned apps, who can use them, and for what purpose. If someone wants to add a new tool, they should go through an approval process and pass a quick security check first. That’s basic vendor hygiene, and ties nicely into your existing vendor management strategies.
Words by themselves won’t prevent a leak, but technology can significantly reduce the risk:
People tend to break rules that are unclear or nonsensical. That’s why your rules should stay human-centered:
Safe examples:
Off-limits examples:
Security training that ends after onboarding is like installing smoke-detector batteries once and calling it good.
Hold short, practical refreshers. Create a five-minute AI safety quiz for each quarter. Add pop-up reminders inside browsers that say, “Are you about to paste client data?”
If you weave education into daily workflows, people remember it when it counts. After all, even the best rules of making an IT strategy only work when humans understand the “why.”
Data policy isn’t a one-time memo, it is a living system. Maintain an AI usage log that records access patterns and red-flags anomalies. If a breach happens, your team should know who to contact, how to isolate the system, and when to notify affected clients.
IBM’s 2024 Cost of a Data Breach Report found that companies facing major security staffing gaps incurred $1.76 million more in breach costs, a reminder that preparedness, whether through people or process, pays off.
Run tabletop drills twice a year. If someone “accidentally” uploads sensitive text, how long before it was caught? The answers will tell you if your policy works.
Most client contracts already define how data must be stored or processed. Your internal AI policy should echo those same expectations. If a client’s NDA forbids third-party processing, block AI use for that project entirely.
When evaluating any AI vendor, ask:
Just a couple of years ago, few companies had “AI policy” on their radar. Today, it is hard to ignore. If your employees are already using tools like ChatGPT, the risks aren’t hypothetical anymore.
At Vudu Consulting, we help businesses build real systems that make security part of everyday work. Whether it’s reviewing how data flows, or locking down AI access points, we meet you where you are.
Let’s figure out what safe, smart AI usage looks like for your team before someone accidentally crosses a damaging line. Contact us to get started.
