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AI & Security
AI for automated code vulnerability detection
A bank tested AI-powered code-scanning tools against a synthetic codebase to measure detection accuracy and false-positive rates before rollout.
4 weeks
to a proven evaluation
The challenge
Security teams needed to know whether AI code-scanning tools could find real vulnerabilities without drowning engineers in false positives, but could not point live tooling at production repositories.
How it ran on NayaOne
1
Synthetic codebase
Representative code with seeded, known vulnerabilities so every tool was scored on identical ground truth.
2
Accuracy benchmarking
Detection rate, false-positive rate and triage effort compared vendor by vendor.
3
Air-gapped workspaces
Tools ran inside isolated developer environments, never touching live source.
Outcomes
4 wks
from kickoff to an evidenced decision
Side-by-side
accuracy and false-positive comparison
0
exposure of production code