Post-legacy SAST and AI code generation: are your controls keeping up?

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.

NHIMG editorial — based on content published by Arnica: Post-Legacy SAST: Modern Code Security Tools for June 2026

By the numbers:

• 84% of developers now use AI tools and AI agents contribute a growing share of net-new code across engineering orgs.
• Legacy SAST tools routinely exceed 50% false positive rates, leaving developers skeptical of every finding.

Questions worth separating out

Q: How should security teams handle code scanning when AI agents generate large volumes of code?

A: They should move security feedback into the same workflow where code is created, especially pull requests and agent-time checks.

Q: Why do legacy SAST tools struggle in AI-assisted development environments?

A: Because they were built for slower delivery cycles and rule sets that assume predictable code patterns.

Q: How do organisations know whether SAST findings are actually actionable?

A: They should ask whether the vulnerable code is reachable from a real entry point, whether the issue affects active paths, and whether the fix changes production risk.

Practitioner guidance

• Measure false-positive drag in developer workflows Track how many findings are ignored, suppressed, or reopened, and compare that with time-to-fix for reachable issues.
• Prioritise reachable code paths over theoretical findings Use call graph and data flow evidence to rank issues by exploitability, then route unreachable or dead-code findings into a lower-priority queue with explicit review policy.
• Move scanning into pull requests and agent workflows Surface findings during review and generation rather than after merge, so developers and AI agents get feedback while code context is still fresh and fix cost is lowest.

What's in the full article

Arnica's full blog covers the operational detail this post intentionally leaves for the source:

• Concrete examples of how post-legacy SAST prioritises reachable findings over theoretical matches
• Workflow guidance for PR-native and agent-time scanning in modern development pipelines
• Implementation detail on hardcoded secret detection across code, config, and CI/CD assets
• Arnica's own framing of hybrid AI SAST behaviour analysis and remediation guidance

👉 Read Arnica's analysis of post-legacy SAST for AI-driven development →

Post-legacy SAST and AI code generation: are your controls keeping up?

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