Source code is not just text. It is architecture, business logic, customer assumptions, internal naming, secret-adjacent context, deployment hints, and sometimes a treasure map to the most important workflows in the company. Treating it like disposable prompt material is a bad habit.

AI security tools can be powerful. They can reason across unfamiliar code, spot repeated mistakes, trace inputs, and ask questions a tired human might miss after the fourth coffee. But if the workflow requires uploading sensitive repositories into a public model pipeline, the risk conversation changes.

Code privacy matters because source tells a story. It can reveal how tenants are separated, how patient records are fetched, how privileged actions are checked, where feature flags live, and which vendors are trusted. Even when secrets are not present, the surrounding logic can be sensitive.

A privacy-aware review model starts with data minimization. Review the approved repository or branch. Keep analysis local or private. Avoid unnecessary retention. Confirm who can access artifacts. Decide how screenshots, logs, and findings are stored. Rotate any temporary credentials when the work is done.

There is also a trust benefit. Developers are more likely to participate honestly when they know the review is not quietly spraying their work across unknown systems. Security review should feel like a seatbelt, not a confessional booth with the windows open.

The practical takeaway is simple: if an AI tool is going to inspect your code, ask where the code goes, how long it stays there, who can access it, and whether the same outcome can be achieved locally. In many cases, local AI provides the coverage benefits while keeping the crown jewels closer to home.