To secure AI-assisted development without slowing it, run fast checks where developers already work: in the editor and the pull request. Block only on findings that are real and fixable, give each finding a clear owner and fix path, and treat generated code like any other untrusted input. Measure friction as seriously as findings.
Why does AI speed change the problem?
More code arrives per developer per day, and some of it was written by a model whose suggestions may contain flaws or name packages that do not exist. NIST's Secure Software Development Framework, SP 800-218, recommends practices to reduce vulnerabilities in released software and address root causes, and it was written as a set of practices to fit any development model. Volume increases the need for automated checks, because review time is fixed.
NIST also published the framework with a goal of a common vocabulary between producers and buyers. In practice, it means your checks can be described in terms a customer or auditor already knows.
What does low friction mean?
It means a developer sees the problem while the code is fresh, understands it in one read, and can fix it without leaving their flow. Friction comes from four sources: slow checks, noisy results, unclear fixes, and blocks that appear late. Remove them in that order.
Speed. If a check takes twenty minutes, people merge around it or ignore it. Keep pull request checks to a few minutes by scanning only what changed, and run deeper scans on a schedule.
Noise. Every false positive teaches people to skip the output. Tune rules, deduplicate findings, and show only issues in changed code during review.
Clarity. A finding should say what is wrong, where, why it matters and how to fix it. Better still, it offers a suggested change.
Timing. Findings that appear after release are costly to act on. Move them earlier.
Where should checks sit?
Three places cover most needs. In the editor, for secrets, risky patterns and dependency choices as code is typed. In the pull request, for dependency changes, code analysis on the diff and policy rules, with a clear pass or fail. In the pipeline and on a schedule, for deeper scans and for newly published vulnerabilities in code already shipped.
How do you handle AI-written code?
Apply the same checks you apply to anyone's code, and a few more. Verify that any new dependency actually exists, is the intended package and is acceptable under your policy, since models can suggest names that are wrong. Keep secrets and personal data out of prompts. Require a human to review the change, and for risky areas such as authentication and payments, a second reviewer.
If agents can run commands or change files, limit what they can reach. Give them scoped credentials, log their actions and require approval for destructive steps.
A constructed example
A team of 15 adopts an AI coding assistant. In month one, pull request volume doubles and the old review process backs up. They set three rules. The editor flags secrets and unknown dependencies before commit. The pull request runs a two-minute check on changed files and dependency changes, and blocks only on secrets, known exploitable vulnerabilities with a fix, and packages outside policy. Everything else posts as a comment and appears in a weekly list. In the next quarter, median time to merge returns to its earlier level and the count of secrets reaching the main branch drops to zero.
How to check or apply this
- Measure current friction: time added by checks, share of findings that are acted on.
- Move fast checks to the editor and pull request.
- Define a short list of block conditions, and publish it.
- Make every finding actionable, with a fix suggestion.
- Verify new dependencies against policy at the pull request.
- Treat AI output as untrusted. Require review, and a second reviewer for sensitive code.
- Scope credentials for coding agents and log their actions.
- Review the block list monthly and remove rules that create noise without protecting anything.
Safeguard offers IDE extensions for VS Code, Cursor (through Open VSX) and JetBrains, an MCP server, policies and gates for pull requests, and reachability analysis to rank dependency findings. Those map to steps 2, 3 and 5. Fix pull requests from autonomous remediation are off by default.
Quick check
Should every finding block a merge?
No. Block on a short list of serious, fixable issues. Report the rest without stopping work, or developers will route around the checks.
Is AI-generated code less secure?
It can contain the same flaws as human code and sometimes invented package names. Treat it as untrusted and apply the same checks.
Sources
Last reviewed: 18 October 2026.
