Topic starter
18/08/2026 2:54 pm
TL;DR: Legacy SAST routinely exceeds 50% false positives and lags AI-driven development, according to Arnica’s analysis, while post-legacy approaches use behavior analysis, reachability, and PR-native or agent-time scanning to surface exploitable findings earlier. The governance issue is no longer coverage alone but whether AppSec controls can keep pace with code produced by human and agentic workflows.
Editorial analysis by NHI Mgmt Group, based on content published by Arnica: “Post-Legacy SAST: Modern Code Security Tools for June 2026”.
By the numbers:
- Legacy SAST tools exceed 50% false positive rates, according to Arnica.
- AI agents contribute a growing share of net-new code across engineering orgs, and 84% of developers now use AI tools, according to Arnica.
Key questions
Q: What breaks when AI SAST is used as a replacement for normal SAST?
A: AI SAST is strongest on logic-heavy defects, but it is slower and less deterministic than traditional SAST.
Q: Why does reachability analysis reduce SAST noise?
A: Reachability analysis reduces noise by checking whether a flagged weakness sits on a code path an attacker can actually traverse.
Q: What do security teams get wrong about post-merge scanning?
A: Teams often treat post-merge scanning as if it were still timely enough to guide fixes, but by then the code may have moved through multiple hands and lost context.
Practitioner guidance
- Adopt reachability-based triage Filter SAST findings by whether the vulnerable code path is callable from a live entry point before routing them to developers.
- Move scanning into pull request workflows Surface findings while code review context is still fresh so the original author can resolve issues without a separate security back-and-forth.
- Evaluate agent-time detection for AI-generated code Treat AI coding agents as high-velocity code producers and inspect their output during generation, not only after merge.
Bottom line: Legacy SAST struggles when AI-assisted development increases code volume, novelty, and the speed at which security feedback expires.
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18/08/2026 3:37 pm
Post-legacy SAST is really about governance latency, not just scanner accuracy. When development cycles compress, a control that produces findings after the code has already moved on is no longer a preventative control in practice. The relevant question is whether security feedback lands while developers can still act on it without rework becoming expensive. Practitioners should treat scan timing as a governance attribute, not a deployment detail.
A few things that frame the scale:
- Companies are dedicating an average of 32.4% of their security budgets to secrets management and code security, with US organisations leading at 40.8%, according to the State of Secrets in AppSec.
A question worth separating out:
Q: When should teams reconsider their SAST model for AI-assisted development?
A: They should reconsider it when findings are consistently dismissed, false positives dominate the queue, or scan latency starts interfering with delivery. Those are signs that the control is no longer aligned with how code is actually produced and reviewed in the organisation.
👉 Read our full editorial: Post-legacy SAST redefines code security for AI-driven development