How We Keep Your AI Safe and Secure

I run security at Heliox. I am writing this without my name on it, which my colleagues find funny, but I think it makes the point better than any certification could. Good security is a habit of assuming things can go wrong, including things with your name on them.

We attack our own systems first

Every system we build goes through an internal attack phase before launch. We try prompt injection. We try to make the model leak data it should not. We try to trick it into doing things outside its job. We feed it hostile documents and watch what happens. If we find a way in, we fix it and try again.

This is not paranoia. It is the same discipline that fire drills are. You practice the bad day before the bad day practices on you.

Your data stays yours

• Client data is never mixed between projects, full stop
• Models trained on your data belong to you, not to us
• We deploy inside your infrastructure whenever you want us to
• Access is logged, reviewed, and revoked the moment a project ends

“The most secure system is not the one with the most features. It is the one where you can explain exactly who can see what, and why.”

What we tell every client

Ask your AI vendor how they tried to break their own system. If the answer is a blank look, that is your answer. Any of us at Heliox can walk you through our last red team exercise on a call. Even me. Especially me.