In regulated environments, “we reviewed it” is not enough.

You need to show:

• what was reviewed
• what checks ran
• what was found
• what was done about it
• who approved exceptions

This is especially important for AI-generated code, where reviewers may not know the full origin of every line.

#What review evidence actually means

Evidence is not a PDF report. It is a reproducible record attached to the change.

A practical evidence model includes:

#1) Automated checks

• security scan results
• secret detection results
• dependency and license scan results
• policy alignment status

#2) Manual review artifacts

• reviewer approval in the PR
• required reviewer assignments for high-risk categories
• exception documentation with approver identity

#3) Context

• what files changed
• what checks passed or failed
• what exceptions exist and why
• timestamps and identity of reviewers

#Why PRs are the right evidence surface

Pull requests are already the place where engineering decisions are made.

They are:

• version-controlled
• auditable
• tied to identity
• linked to the actual code change

Adding security evidence to the PR keeps governance close to the work, instead of creating a parallel process.

#What auditors want to see

Audit and compliance teams typically need:

• proof that a control existed
• proof that it ran
• proof that findings were addressed

For AI-generated code, they may also need:

• evidence that AI-assisted changes were treated with appropriate scrutiny
• evidence that secrets and licensing were checked
• evidence that high-risk changes received extra review

If this evidence lives in the PR, it is easy to produce. If it lives in a separate system, it is easy to lose.

#Lightweight implementation

1. Require checks on every PR (security, secrets, license)
2. Surface findings as PR comments or statuses (pass/warn/block)
3. Require human approval for block-level findings or architectural changes
4. Log exception rationale in the PR or linked ticket
5. Retain PR history as the evidence record

No additional paperwork needed.

#The Cyblox view: evidence is a first-class output

GenAI Code Security produces review evidence as a core output:

• check results attached to PRs
• policy pass/fail with rationale
• exception audit trail
• historical record of what was reviewed and when

More at: /solutions/security/genai-code-security/.

#Closing thought

Trust is good. Evidence is better.

In regulated environments, you need both.

If AI-generated code is entering your repos, add pre-merge security, secrets, licensing, and policy checks—with review evidence—without changing how developers work.