A startup with no security team can cover the main risks of AI-written code in a week: scan for secrets before commits, scan dependencies in CI, review AI-generated diffs like any contractor's, and limit what coding agents can run. You do not need a hire to start. You need a short routine that people actually follow.
AI assistants write plausible code quickly, and plausible is not the same as safe. The cheapest defense is making the checks automatic, so the review burden does not depend on remembering.
Why does AI-generated code need extra checking?
One well-known measurement is old but instructive. A 2021 study of GitHub Copilot, "Asleep at the Keyboard?", generated 1,689 programs from 89 scenarios based on high-risk weakness categories and found approximately 40 percent were vulnerable. Models and tools have changed since then, so do not read that figure as a current rate. The lesson that holds is that generated code inherits patterns from public code, including bad ones, and nobody should merge it unread.
There is a second risk beyond the code itself: assistants suggest packages. A name that looks right may be misspelled, abandoned or malicious, and a model has no reason to know. Dependencies deserve their own check.
What is a five day plan?
Day 1: secrets. The most common startup disaster is a leaked key. Turn on secrets scanning for repositories and on developer machines before commit. The Safeguard CLI has a detect command for secrets and misconfigurations, and its VS Code and Cursor extension has a command to check staged commits for secrets. Any good scanner works; the point is to block the commit, then rotate anything that already leaked.
Day 2: dependencies. Generate an inventory and scan it for known vulnerabilities and licenses. You can try this with no signup by running the free Gold scan at gold.safeguard.sh/scan, which checks up to 100 dependencies in your browser. For ongoing use, add a scan to CI so new packages get checked when they are added.
Day 3: pull request rules. Require review for every merge, including AI-written code. Add a short checklist to the pull request template: new dependencies listed, no secrets, input validated, authorization checked. NIST's Secure Software Development Framework, SP 800-218, is the reference for what a mature version of this looks like, and you can borrow its practices gradually.
Day 4: agent permissions. If agents run commands or edit files, set them to ask before running shell commands, writing outside the repository or making network calls. Keep production credentials out of agent environments. OWASP describes excessive agency as the combination of too much functionality, permission and autonomy, and the fix is to cut all three.
Day 5: basics for when something goes wrong. Write one page: who is on call, where logs live, how to rotate keys, how to take a service offline. Test the key rotation once.
What about your own AI features?
If you ship features built on language models, read the OWASP Top 10 for LLM applications. Two entries cover most early mistakes: prompt injection (LLM01), where external content steers the model, and excessive agency (LLM06). Keep authorization checks in code, not in prompts.
How do you keep this lightweight?
Automate every check you can so it runs without anyone's attention. Fix only what a tool shows as exploitable or exposed first, then widen. Keep a short list of exceptions with expiry dates. Review the routine every quarter, and when your team grows, hire for the gaps you have actually seen.
What are the limits?
This routine finds known issues and obvious mistakes. It will not catch design flaws, business logic bugs or a determined attacker. A scan with no findings can mean a scan that did not run, so confirm it runs. If you handle regulated data, get specialist help early, because the plan above is a floor.
What to do
- Turn on secrets scanning for repositories and pre-commit, and rotate anything found.
- Add dependency scanning to CI.
- Require pull request review for all code, including AI-written code.
- Set agents to ask before running commands or touching the network.
- Keep production credentials out of developer and agent environments.
- Write the one-page incident basics and test key rotation.
Quick check
Is AI-generated code less secure than human code?
The 2021 Copilot study found about 40 percent of 1,689 generated programs vulnerable in its scenarios. Tools have changed since, so treat it as a reason to review, not a current rate.
What is the first check a tiny team should add?
Secrets scanning before commit. A leaked key is quick to exploit and expensive to clean up.
Sources
- Pearce et al., Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code Contributions
- NIST SP 800-218, Secure Software Development Framework
- OWASP LLM06:2025 Excessive Agency
- OWASP LLM01:2025 Prompt Injection
- Safeguard CLI usage
- Safeguard IDE support
Last reviewed: 18 October 2026.
