AI coding assistants do not change what SOC 2 asks, but they raise the pace of dependency changes and can suggest packages nobody has vetted. Keep the same pull request review, scanning and approval rules for AI-authored code, and add a check for new packages. Document the practice in your change management policy.
What actually changes
An assistant can add a library in seconds, rewrite a lockfile, or suggest an install command that pulls a name it half remembers. The control surface is the same: a change enters through a pull request, gets reviewed, passes checks and ships. The risk is volume and attention. Reviewers see larger diffs and trust fluent code.
SOC 2 evaluates the controls you describe, so the first step is to describe how AI generated changes fit. A sentence is enough: changes authored with AI assistance follow the same review and testing requirements as any other change, and the author remains accountable.
Three risks specific to dependencies
- Unvetted new packages. The assistant picks a package by pattern, not by maintainer reputation or advisory history.
- Names that do not exist or were registered by someone else. Attackers can register a plausible name an assistant tends to suggest. Always confirm a new package is the one you meant.
- Silent lockfile churn. A tool that runs an install may upgrade many transitive packages as a side effect.
Controls that handle them
| Risk | Control | Evidence |
|---|---|---|
| New package added | Pull request check that flags new direct dependencies, with approver | PR check result, approval |
| Wrong or malicious name | Reviewer confirms registry page, maintainer, age and downloads | Review comment or checklist |
| Lockfile churn | Frozen installs in CI and a diff review of the lockfile | CI logs, PR diff |
| Unreviewed merges | Branch protection requiring approval | Branch rules export |
GitHub's dependency review is an example of a pull request view that shows which dependencies were added, removed or updated, with release dates and known vulnerability data, including indirect dependencies in lock files. Whatever tool you use, make sure the reviewer sees the dependency delta, not only the source diff.
Rules of thumb for a policy line
- A person approves every new direct dependency, whoever or whatever suggested it.
- Assistants run with least privilege, with no production credentials and no ability to merge.
- Agent or assistant actions on your systems should be attributable to an identity you can review later.
- Keep chat transcripts out of the evidence process unless your auditor asks. The pull request is the record.
What not to claim
Do not tell buyers that AI generated code is "reviewed by AI" and therefore safe. An automated check is a control layer, not an approval. Keep human sign off where your policy promises it.
Try it yourself
Open the demo to see how an assistant can work with Safeguard in the loop. Look at what the assistant is able to check before a dependency is accepted.
Click through it at your own pace, or open it full screen.
Where Safeguard fits
Safeguard's MCP Guardrail, agent identity, policies and gates, and dependency scanning are built for this: they help control what assistants can do and check dependencies before merge. Free scan: https://gold.safeguard.sh/scan.
Quick check
Do I need a separate SOC 2 control for AI coding tools?
Often not. Extend your existing change management and access policies to mention them, and apply the same review rules.
How do I catch a package that does not exist?
Require a human to open the registry page for each new direct dependency and confirm the name, maintainer and history.
Sources
Last reviewed: 19 October 2026.
