Can AI Agents Satisfy SOC 2 Code Review Requirements?

Code reviews are one of the most common controls used by companies looking to satisfy SOC 2's change management requirements.

Traditionally, a code review would involve a human engineer reviewing the work of another before the code is put live. But increasingly, AI agents are being used to review pull requests, and merge code into production.

But does an AI agent code review satisfy SOC 2's change management requirements?

Short Answer: Yes, If the Review Still Meets the Control Objective

Can an AI agent satisfy SOC 2 code review requirements? Yes, so long as the review process still meets the control objectives.

SOC 2 doesn't prescribe a specific reviewer type (human or agent). It looks for evidence that code changes are reviewed before deployment, and that the review is documented. It doesn't state that a human must do the review, only that it happens, and you can prove it happened.

SOC 2 is a principles-based framework. It doesn't hand you a list of set requirements to check off the list. It sets objectives and asks you to explain how you meet them and provide evidence to show you're doing what you say you are.

So when it comes to whether an AI agent can satisfy a code review requirement, the answer is often yes, providing that your process actually reviews the code and produces evidence that proves it.

In most cases, the agent can assist with the review, but a human owner should remain accountable for approving the change — unless the company has formally defined and tested an automated approval process.

So an agentic review workflow could look like this:

Engineer writes code → PR created → independent AI review → automated tests/security checks → findings resolved → approval → merge → deployment

There's also a difference between AI agents being used for code reviews and authorization/approval. Here we're talking about AI agents reviewing code (commenting, running checks, flagging issues on a pull request), not approving and merging code or using an autonomous agent that writes and merges its own code to production without human oversight.

What SOC 2 Requires for Code Changes

Under SOC 2's Security common criteria, code changes must be reviewed, approved, tested, and traceable before reaching production:

• Reviewed: The code has been evaluated before merged into production.
• Approved: The code was signed off after review.
• Tested: The new code passes checks (CI, security scans) before it's deployed to live.
• Traceable: The full workflow is stored and there's evidence code was reviewed.

Code change reviews fall under change management in SOC 2 — part of the Security common criteria as CC8.1, which says the entity "authorizes, designs, develops or acquires, configures, documents, tests, approves, and implements changes."

CC8.1 doesn't state that the code review must be completed by a human. Just that the change needs to be authorized and approved, that tests run, and that you can prove all of it after the fact.

Where you need to be careful, though, is with the wording in your control description. If your policy states something like, "Production code must be peer-reviewed by another engineer," then using an AI agent to review code changes means you're not operating the control as documented.

What You Should Say in Your Change Management Policy

If you're using an AI agent to review code changes, you'll need to make it clear in your change management policy. A statement along the lines of the below should suffice, but be sure to check it aligns with your process and get sign-off from security leadership before changing your policy:

Code changes are reviewed before production release. Reviews may be performed with assistance from automated or AI-based tooling, but approval responsibility remains with an authorized team member. Review activity and approval evidence are retained in the version control system.

Every clause in that statement is playing a specific role:

• "Reviewed before production release" states the objective.
• "Assistance from automated or AI-based tooling" states that you use AI in your code review processes.
• "Approval responsibility remains with an authorized team member" pins accountability to a person or people, because ultimately, the code you ship is the responsibility of your team.
• "Retained in the version control system" is your traceability and your evidence.

What Evidence Should You Keep?

Whether a human or AI agent is completing the review, the evidence is essentially the same as what your version control system already produces. If you use GitHub, GitLab or similar tooling, you'll have almost everything you need in place.

Can an AI Agent Replace Human Code Review Approval?

Sometimes. It's possible for an AI agent to review code and merge to production with no humans involved in the process. But it's harder to justify, and we usually advise against an AI-only process for production-impacting changes.

If you want to use an AI agent as the reviewer and approver, it's possible, but the process needs to be documented and thoroughly tested. Before letting an agent review, approve, and merge code, you should know:

• Which changes are eligible for automated approval
• What the agent actually checks
• How exceptions get handled
• Who owns the process
• How you monitor the agent's performance over time

If you can define, document, and test against all of those questions, an automated approval workflow is defensible. But for most SOC 2 programs, the cleaner answer and better workflow is: AI assists with code reviews, humans approve.

How to Make AI-Assisted Review Audit-Ready

To ensure your AI-assisted code review process is audit-ready, there are a number of things you need to consider: a real bot identity, branch protection, keeping human accountability clear, defined scope, and a policy that matches what you actually do.

Here's what each one means:

• Give the agent a real identity: Any AI agents conducting reviews should have a named bot or service account so you can attribute actions directly to agents. Agents shouldn't use a shared login or act under a human's account.
• Branch protection: Branches where AI agents are reviewing code should be protected and require pull request approvals and checks before merging to production.
• Keep human accountability clear: Humans should always own accountability for changes shipped to production code. If you want to have AI agents own the process from review to live code, it's possible, but requires you to document your controls and policies around it.
• Define the scope: Say whether the agent reviews everything, or only certain repositories, languages, or risk categories.
• State how you use AI in your policies: Make sure your change management policy describes the workflow you actually run.

How to Use an AI Agent Reviewer

SOC 2 doesn't explicitly require a human to review your pull requests. It requires that changes are reviewed, approved, tested, and traceable before they're shipped to live production code. An AI agent can do the review stage. But, in most cases, a human should still own the approval before code reaches production.

The key thing to remember is that your processes need to match your policies. So if you're using AI to review code, make sure that's reflected in what you say around your change management controls. Keep your policy honest, ensure there's evidence, and there's no reason you can't use an AI agent for code reviews.

At Workstreet, we offer expert SOC 2 implementation to get you audit-ready, fast. From Type I to Type II, we can guide you through every step of the process, including shaping your policies, and guiding you on how and where to use AI. If you're looking at SOC 2 or want to know how to use AI in your code reviews (or anywhere else in your workflows), get in touch with our team here.

AI Agent Code Reviews for SOC 2 FAQs

Can the AI agent be the only reviewer?

It's possible to have AI review and merge code. But it's not something we often advise. If you want to go down this route, you need clear boundaries, policies around which types of changes can be committed by agents, plans for escalation, and humans accountable for the overall process.

Do AI review comments count as evidence?

AI code review comments can be used as supporting evidence. Primary evidence is generally the pull request itself, which will include the review, approval, and merge timing.

Do we need to tell the auditor we use AI?

Your policies should match your processes. There's no reason to hide your use of AI. Explain how it's part of your review workflow, and how you preserve approval, traceability, and accountability. Concealing it is a much bigger risk than the AI itself.