AI CODING IS JAMMING SECURITY QUEUES BECAUSE PROCESS, NOT TOOLING, IS MISSING
A New Stack article argues two process failures with AI-generated code are clogging security review pipelines and slowing releases. The piece from The New Stac...
A New Stack article argues two process failures with AI-generated code are clogging security review pipelines and slowing releases.
The piece from The New Stack says teams often ship AI-written code without built-in security checks, then dump risk onto manual review queues at the end. That combo creates backlogs and brittle releases. Read it here: The 2 failures with AI coding that are creating security bottlenecks.
The fix is mostly procedural. Treat AI-suggested code as untrusted input, enforce automated gates early, and track where AI assists are used so scanners and reviews focus where risk increases.
AI-generated code increases change volume and variance, which exposes gaps in existing security gates and can swamp human reviewers.
Without early automated checks, teams defer risk to late-stage reviews, causing release delays and inconsistent security quality.
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terminal
Tag AI-assisted commits and compare review time, vuln density (SAST/SCA/secrets), and rollback rate vs. non-AI commits for 2–4 weeks.
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terminal
Enable pre-commit and CI gates (SAST, SCA, secret scan, license policy) on AI-touched files and measure security queue length before/after.
Legacy codebase integration strategies...
- 01.
Add policy-as-code gates incrementally to high-risk services; block on critical issues, warn on medium initially to avoid deadlock.
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Auto-label PRs with "ai-assisted" and route to security-savvy reviewers; create an exception workflow with time-bound approvals.
Fresh architecture paradigms...
- 01.
Ship repo templates that default-on SAST, SCA, secret scanning, and provenance checks; require tests for AI-suggested code paths.
- 02.
Define dependency allow-lists and minimal permissions early; codify threat models alongside service docs to anchor reviews.