What "safe" means when you ship GenAI to an enterprise

When safety comes up in a customer conversation, it almost never means what the AI labs mean. The lab conversation is about scheming, deception, capability overhang. The enterprise conversation is about audit logs, PII, license risk, the model citing a competitor in a customer-facing channel. Both are real. They don't translate.

Three things enterprise safety actually means in practice

First: provenance. Every assertion the model makes needs to be tied back to a source the business is willing to defend. "Where did this number come from" is a question you will be asked, and "the model" is not an acceptable answer.

Second: containment. The agent's tool surface is also its attack surface. Tools that can read should not silently also write. Tools that can write should leave a trail. The blast radius of a confused agent should be smaller than the blast radius of a confused intern.

“The blast radius of a confused agent should be smaller than the blast radius of a confused intern.”

Third: evaluation. Not eval-the-benchmark — eval-the-product. What does failure look like for this specific deployment, and how would you notice it before your users do? Most teams ship without being able to answer that, and it shows up later as a series of one-off incidents nobody can quite explain.

Where the two conversations meet

They meet at the same place: humility about what the model is actually doing. Whether you're worried about a model lying to a regulator or a model lying to your VP of Sales, the engineering moves are surprisingly similar. Narrow the tools. Watch the traces. Trust the evals more than the demos.