Topic starter
01/08/2026 8:54 pm
TL;DR: Nearly 70% of organisations have found flaws tied to AI-generated code, 1 in 5 reported serious incidents, and engineers spend 6.1 hours a week triaging findings with 72% of that time wasted on false positives, according to Aikido’s 2026 comparison. The real issue is not scan coverage alone, but whether SAST can suppress noise, prioritise reachability, and fit developer workflows at commit time.
Editorial analysis by NHI Mgmt Group, based on content published by Aikido: “Top SAST tools 2026”.
By the numbers:
- Nearly 70% of organisations say they have uncovered flaws tied to AI-generated code.
- 1 in 5 organisations reported serious incidents linked to AI code.
- 72% of that triage time is wasted on false positives.
Key questions
Q: How should security teams reduce repetitive false positives in SAST programs?
A: Start by encoding the context your scanner cannot infer reliably.
Q: Why do AI coding agents make SAST governance harder?
A: AI coding agents increase code volume and can generate findings faster than manual review can absorb, which magnifies alert fatigue.
Q: What are the signs that a SAST or DAST program is not working well in practice?
A: Common warning signs include excessive false positives, developer fatigue, missed runtime issues, and scans that do not keep pace with delivery cycles.
Practitioner guidance
- Prioritise reachable findings over raw alert counts Choose SAST tools that score findings based on execution path, not just CWE match or severity labels.
- Test autofix quality on your own repositories Validate AI-generated fixes against representative code before allowing them into production workflows.
- Require native pull request and IDE integration Make sure findings appear where engineers already work, including editor plugins, PR comments, and ticketing systems.
Bottom line: AI-generated code has pushed SAST from a detection question into a governance question about how much signal humans can still absorb.
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01/08/2026 9:02 pm
AI-generated code has turned SAST into a signal-management problem, not a scan-volume problem. The article’s core finding is that organisations are now reviewing far more machine-produced code, which amplifies the cost of noisy output. A control that cannot distinguish reachable issues from theoretical ones will eventually be bypassed by developers. Practitioners should treat alert quality as the primary AppSec governance metric.
A question worth separating out:
A: Choose AI-native SAST when you need reasoning over business logic, access control, and multi-step exploit chains that rule-based scanners often miss. Choose AI-assisted SAST when you already have deterministic scanning and want better triage, broader language support, or faster developer workflows. Many teams benefit from both, but the right mix depends on whether depth, scale, or reproducibility is the primary goal.
👉 Read our full editorial: Top SAST tools 2026 show AI-generated code is raising AppSec noise