Snyk's Remediation Agent embeds security context into frontier models via an intelligence layer, improving SAST fix rates from ~72% to ~82% and SCA rates by ~94%, reducing token spend 61% in the process.
July 1, 2026
Summary
Security backlogs are growing faster than teams can triage—65–70% of production code is AI-generated and nearly half contains exploitable vulnerabilities. This shifts the bottleneck from detection to remediation, requiring tools that act on findings rather than surface more of them. Naive AI-assisted fixes fail because models lack context on dependency versions, breakability, and reachability; Snyk's intelligence layer bridges that gap.
Why it matters
Security backlogs are growing faster than teams can triage—65–70% of production code is AI-generated and nearly half contains exploitable vulnerabilities. This shifts the bottleneck from detection to remediation, requiring tools that act on findings rather than surface more of them. Naive AI-assisted fixes fail because models lack context on dependency versions, breakability, and reachability; Snyk's intelligence layer bridges that gap.
Implementation verdict
Replaces manual triage and naive LLM-to-fix piping for SCA issues; SAST/Container/IaC still in development. Requires running experimental CLI locally with access to frontier or self-hosted models. Start here if your team is drowning in dependency upgrade backlogs—the human-in-the-loop design (you review every change) makes it low-risk to evaluate. Not ready for fully autonomous merges yet, but the benchmarks justify trying it now on SCA.
Sources
Dev Signal
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