The Real Risk
Here's what keeps me up at night about AI security: data leakage. You feed confidential information into an AI system. That information ends up in the wrong place. That's bad.
It's happened. A company used ChatGPT to optimize their code, pasted in proprietary algorithms, and Samsung's trade secrets ended up in the training data. Oops.
The good news: This is avoidable if you're thoughtful.
The Security Rules That Matter
1. Never Feed Confidential Data to Public AI Services
Don't paste your customer data into ChatGPT. Don't feed proprietary information into Claude. Don't use public services for anything sensitive.
This is the rule. It's simple. Many companies break it anyway.
2. If You're Using AI, Use It On Your Infrastructure
Build agents on servers you control. Use AI models that run on your infrastructure or through secure APIs with clear data handling agreements. Know where your data lives.
At Dig Solutions, all our agents run on our infrastructure or through APIs with explicit data protections. We're not uploading client data anywhere it shouldn't go.
3. Audit What the AI Is Seeing
What data does the agent need to do its job? Give it exactly that. Not more. Not less. If it doesn't need to see a customer's social security number, it shouldn't.
This is the principle of least privilege. Apply it ruthlessly.
4. Monitor for Errors
AI agents can hallucinate. They can make stuff up. They can misunderstand context. Build oversight into your process. "Human reviews everything the agent outputs." That's not a weakness. That's a requirement.
The Stuff That's Overblown
"AI will steal our secrets." Possible, but preventable with basic practices.
"AI is inherently unreliable." No. Well-built AI is reliable. Poorly-built AI is unreliable. The tool isn't the problem. The implementation is.
"We can't use AI because we have compliance requirements." False. You need to be thoughtful about how you implement it. But it's doable.
What You Actually Need
A clear data security policy. "Here's what data can be used in AI systems. Here's what can't. Here's how we monitor it." That's it. Not hundreds of pages of policy. Just clarity.
Most of the companies I've worked with that had security concerns resolved them once they understood: (1) We control where the AI runs, (2) We control what data it can see, (3) We audit the output. Those three things cover 95% of legitimate concerns.
The Conversation to Have
Before you build an AI system, ask your security/compliance team one question: "If we build this on our infrastructure, with data encrypted in transit and at rest, and we have humans reviewing the output, what are your actual concerns?"
Usually you'll find the concerns are solvable. Sometimes they're not. But you won't know until you ask.