A clip from our AI Safe Sandbox webinar (September 2025), which we also ran as the Responsible AI Roadmap webinar. Recorded in 2025: AI tools’ settings and features have changed since.
A traffic-light method for classifying data before it goes into an AI tool, and the two reasons data safety comes first.
Key takeaways
- Red: never put it into a public AI system. Client health information, unreleased intellectual property.
- Amber: drafts or anonymized notes. Use carefully, with private details masked.
- Green: already public or general knowledge. Generally safe, and enough for many useful tasks: researching, summarizing, translating, even decoding obscure computer errors.
- Two risks drive this. You can’t verify how AI companies store your data, and by default your data may train future models. Turn off training in public chats.
Try this with your team
Sort ten documents your team handled this week into red, amber and green. Where did you disagree? That disagreement is where your policy needs a line.
Going further
Our Ethical AI Guardrails work turns a stoplight like this into your team’s data rules. Check your current policy first with the free AI Policy Health Check.
More from this series
- AI Safe Sandbox Webinar, Part 1 (September 2025)
- AI Safe Sandbox Webinar, Part 2 (September 2025)
- Responsible AI Roadmap Webinar: Identifying Safe Data Practices
- Responsible AI Roadmap Webinar: Responsible AI Policy Language
- Responsible AI Roadmap Webinar: Good Preparation
- Responsible AI Roadmap Seminar: The Nonprofit Use Case Moment
Transcript
Lightly edited from YouTube’s automatic captions for readability. Wording and order are the speakers’ own.
One way to think about the data is a traffic light system to classify it. Red data would be like client health information or unreleased intellectual property. That’s something you would never want to put into a public AI system.
Amber data would be like drafts or anonymized notes. These can be used carefully if the private information is masked.
Green data is things that are already public, already on the internet or general knowledge, and that is generally safe. As a starting point, providing data that’s already online or not a secret is enough to get a lot of very useful applications, ranging from researching topics to summarizing articles to translating languages, including one of my favorites: obscure computer errors into English. It’s actually pretty good at that sometimes.
The reason safe data practice is so important, and why we have it first, is that there are two main sources of risk with these systems and data. First, what the AI companies are storing isn’t necessarily stored at the highest security compliance.
“Or we don’t have any way of knowing, really.”
Yeah. “Black box there.” There are Silicon Valley companies that move fast and break things. They’re not companies you want to trust with very sensitive information. That’s the one reason.
The other is that your data is used to train new models by default, which means what you paste in can show up in a future user’s response. That’s why we say always turn off training in public chats. According to their policies, that should eliminate that risk.