When assistants generate much of your code, evaluate static analysis on volume, speed and placement rather than on new features. Test large diffs, run checks before code is committed as well as at pull request time, and confirm secrets and dependency coverage. Do not assume an assistant's own suggestions have been checked.
What changes and what does not
AI assistants raise the amount of code each developer proposes, and reviewers see larger diffs more often. The flaws are mostly the usual ones: injection, missing authorisation checks, unsafe use of cryptography, hardcoded credentials. What changes is the review load and the speed at which they arrive.
So the evaluation questions are less about novel detection and more about whether the tool keeps up and sits in the right places.
Tests worth adding
Large diff pull requests. Open a pull request with several hundred changed lines. Time the scan. See whether the result view stays usable or becomes a wall of comments.
Pre commit or editor feedback. If code appears in the editor from an assistant, the earliest check is there. Test the IDE plugin: does it flag issues while code is being written, how heavy is it, and does it work with your editor?
Missing checks, not just bad calls. A common failure in generated endpoints is a missing authorisation check. Static tools often cannot detect an absent check, because nothing in the code looks wrong. Be honest about this limit, and plan code review and tests to cover it.
Hardcoded secrets. Generated examples sometimes carry placeholder keys that look real, or real ones copied from context. Check that the tool detects secrets and that you can block them before merge.
Dependency suggestions. Assistants may propose packages that are old, abandoned or unfamiliar. Ask whether the tool also checks dependencies, and whether it covers the manifest changes that arrive with the generated code.
Pair static analysis with something else
No single scanner catches everything. A reasonable minimum for a repository with heavy assistant use is static analysis for first party code, dependency scanning with reachability, secrets detection, and a human review that looks specifically at access control. Tests that assert who may call what are cheap and catch exactly the failures static tools miss.
Policy questions
Decide whether code from an assistant gets a different bar. Some teams label such pull requests and require a second reviewer. Others apply the same rules to all code. Whichever you pick, make sure the tool can enforce it, for example through branch protection and required checks.
Also ask where code goes during scanning. If a vendor sends code to an external model to explain a finding, you need to know that, and you should be able to turn it off.
What not to buy for this reason
Be wary of anything sold on the premise that AI code is a wholly new category requiring a wholly new tool. The better question is whether your current checks run early, run fast, and cover the gaps in the previous paragraph.
Try it yourself
Click through it at your own pace, or open it full screen. Follow one finding from detection to fix and note how early it appears.
Quick check
Do I need a special scanner for AI generated code?
Usually not. You need checks that run early and fast on large diffs, plus tests and review for access control, which static tools struggle to see.
Where should the first check run?
In the editor or pre commit if possible, then again at pull request and on a schedule.
Safeguard offers SBOM and dependency scanning, policies and gates, and an MCP Guardrail, and the free scan is at gold.safeguard.sh/scan.
Sources
Last reviewed: 19 October 2026.
