A sales manager builds an AI-assisted workflow to route leads. It connects to customer records, summarizes inbound requests, and posts updates to a shared channel. The workflow saves time, reduces manual work, and soon becomes part of the team’s daily routine. Across the same organization, employees are building useful apps, agents, and automations faster than existing processes can support them.

If you work in security, that success introduces a new set of questions. Whose credentials does the workflow use? Which records can it access? Who can see its output? If it starts exposing information it shouldn’t, can you trace what happened and stop further access?

Wild code takes root when useful builds spread faster than the processes for owning, securing, and maintaining them. It isn’t simply another name for unapproved software. Employees are creating new capabilities, not just adopting existing products. A small experiment can become an operational dependency before security knows it exists.

The risk goes beyond vulnerabilities in generated code. Even code that works as intended can operate with too much access or expose data to the wrong audience:

Employee education, sandboxes, and AI steering committees are a good start, but they aren’t enough to control wild code on their own. A more sustainable approach builds controls into the environment itself: what employees can connect to, what their apps and agents can do, and how that work is monitored once it’s running.

Education, sandboxes, and steering committees all have a role. The mistake is expecting them to govern software they may never encounter, or control what happens after it starts running.

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