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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.
NHIMG editorial — based on content published by Aikido: Top SAST tools 2026
By the numbers:
- nearly 70% of organizations say they’ve uncovered flaws tied to AI-generated code
- 1 in 5 organizations reported serious incidents linked to AI code
- 72% of that time wasted on false positives
Questions worth separating out
Q: How should security teams implement SAST in fast-moving development pipelines?
A: Start by enforcing commit and pull-request scanning where code is already changing, then route results into the developer tools used for review and ticketing.
Q: Why do false positives make SAST less effective at scale?
A: False positives consume analyst time, slow down merges, and train developers to distrust security output.
Q: What do teams get wrong about AI-assisted SAST?
A: Many teams assume AI automatically improves detection, when in practice it usually improves triage and remediation.
Practitioner guidance
- Measure SAST by reachable findings, not raw alert volume. Track the share of findings that are reachable in production paths, then compare that with false-positive rates and mean time to close.
- Inline findings into developer workflows. Require IDE, pull request, and ticketing integration for any scanner used as a gate.
- Weight identity and secrets logic more heavily. Assign stricter review thresholds to authentication code, session handling, token validation, and secret references because defects in those paths can become access-control failures or credential exposure events.
What's in the full article
Aikido's full post covers the operational detail this post intentionally leaves for the source:
- Per-tool comparison tables with pricing, deployment constraints, and language coverage details for SAST selection.
- Workflow-specific remediation examples showing how findings land in IDEs, pull requests, and ticketing systems.
- Tool-by-tool notes on false-positive behaviour, scan speed, and enterprise governance features that affect adoption.
- The full decision matrix for teams choosing between developer-first and enterprise-grade SAST models.
👉 Read Aikido's full comparison of top SAST tools for 2026 →
Top SAST tools 2026: are your code review controls keeping up?
Explore further
01/08/2026 9:02 pm
AI-generated code has turned SAST from a developer convenience into a governance control. When code volume rises faster than human review capacity, the deciding factor is whether the scanner can suppress noise, prioritise reachability, and keep remediation inside the delivery workflow. That changes AppSec from a detection-only function into a throughput control. For practitioners, the question is no longer whether SAST exists in the pipeline, but whether it influences merge decisions reliably enough to matter.
A question worth separating out:
Q: What should organisations prioritise first, coverage or workflow integration?
A: Workflow integration usually comes first because even accurate findings fail if developers cannot act on them where they work. Coverage matters, but a scanner that is difficult to use will be bypassed. Inline feedback, PR comments, and ticketing links are the controls that make SAST enforceable.
👉 Read our full editorial: Top SAST tools 2026 show AI-generated code is raising AppSec noise
