Record every AI fix suggestion with an outcome (accepted, edited, rejected), a one line reason, the reviewer and the checks run. After a month the pattern shows where the tool helps and where it fails, and you have a concrete record to show customers or auditors.
What a log is for
Teams adopt AI fix suggestions on trust and then argue about whether they work. A log replaces the argument with data from your own code. It answers practical questions. How often are suggestions accepted as is? Which kinds of fixes need rework? Which reviewers catch the most problems?
It is also a record of human oversight. If a customer asks how you control AI generated changes, a dated log of reviews is more convincing than a policy sentence.
Fields to capture
| Field | Example | Why |
|---|---|---|
| Date and finding | Date, finding id, weakness class or package | Find it again |
| Source | Assistant, bot or IDE suggestion | Compare sources |
| Change type | Dependency bump, code change, config | Spot weak categories |
| Outcome | Accepted, edited, rejected | The headline metric |
| Reason | One line in plain words | The learning |
| Checks run | Reproduction, rescan, test diff reviewed | Show diligence |
| Reviewer | Name or role | Accountability |
| Follow up | Reverted within 30 days? | Catch late failures |
Keep it small. If it takes more than a minute to fill in, people will skip it.
Where to keep it
The pull request itself is the best home. A template with a few checkboxes and a reason field is enough, and the data can be exported later. A spreadsheet works for a small team. Choose one place and do not split it.
Reasons worth standardizing
A short list makes later counting possible: did not fix root cause, out of scope edits, tests weakened, wrong or missing version, behavior change, style only, duplicate, other. Free text can sit beside it.
OWASP's guidance on secure coding with AI describes several of these failure modes, such as out of scope edits, deleted or weakened tests and outdated or nonexistent packages. Using them as categories gives your log the same vocabulary as published guidance.
Reading the log
Review monthly for ten minutes. Look for three things:
- Which change type has the highest rejection rate? Consider tightening review there or turning off suggestions for it.
- Which reasons repeat? A repeated reason suggests a process fix, such as a stricter checklist.
- Do reverts cluster? If accepted changes keep being reverted, your review is missing something.
Be careful with rates on small samples. Ten suggestions will not give a stable percentage. Use the log to find stories, not to publish a headline number.
Evidence for compliance work
An audit trail of AI assisted changes supports change management questions in many frameworks, but do not describe it as meeting any requirement until you have mapped it with your auditor. Safeguard's compliance and evidence automation can help gather change records, and autonomous remediation, which is off by default, can attach context to each fix pull request.
See questions to ask an AI assistant about its fix for what to capture in the explanation field.
Quick check
Should rejected suggestions be logged too?
Yes. Rejections carry the most information about where the tool falls short.
Sources
Last reviewed: 19 October 2026.
