Many organizations start their AI code security program with the wrong metric:

“How much AI-generated code do we have?”

That number is interesting but not actionable. Knowing volume does not tell you risk.

The right metrics focus on outcomes:

“Is AI-generated code introducing more risk, and are we catching it?”

#Outcome metrics that work

#Pre-merge catch rate

• secrets prevented from reaching main
• vulnerable dependencies blocked
• policy violations caught in PR

This measures the control, not the threat.

#Time-to-fix

• median time from finding to resolution
• variance by severity

Fast fix times mean your process is healthy, not just your detection.

#Exception rate

• how often policy is bypassed
• who approves exceptions
• how long exceptions persist

High exception rates usually mean policy is misaligned with reality.

#Review coverage

• percentage of AI-assisted PRs that receive automated checks
• percentage that receive human review where required

Coverage tells you whether controls are actually applied.

#Risk per PR

• average number of findings per AI-assisted PR
• trend over time

The goal is a downward trend, not zero. Zero usually means you’re not looking.

#What not to measure

#“Lines of AI-generated code”

Volume is not risk. A thousand safe lines are less risky than ten dangerous ones.

#“Number of scans run”

Scans are activity. Findings caught are outcomes.

#“Developer satisfaction with AI tools”

Useful for adoption, not security.

#Reporting by audience

#For AppSec

• catch rate by category (secrets, dependencies, patterns)
• mean time-to-fix
• exception volume and aging

#For engineering leadership

• risk per PR trend
• review coverage
• velocity impact (are controls blocking or surfacing?)

#For compliance

• evidence coverage
• audit trail completeness
• policy exception documentation

#The Cyblox view: measure control quality, not AI volume

GenAI Code Security produces the metrics above as operational outputs:

• findings by category and severity
• trend dashboards
• exception workflows with approver identity
• evidence exports for audit

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

#Closing thought

You cannot manage what you do not measure.

But you also cannot manage what you measure poorly.

Focus on outcomes, not volume, and your AI code security program will stay aligned with actual risk.

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.