SOC 2 AI change-management policy template
If AI writes or assists production code in your business, a SOC 2 auditor will ask how those changes are reviewed differently. This is a starting template that answers that question — AI as an authorized contributor, a human review gate, testing parity, and an audit trail. Copy it, fill the brackets, and adapt it with your auditor. It maps to the change-management criterion (CC8.1). For the why behind it, read SOC 2 and AI-generated code.
Policy template
AI CHANGE-MANAGEMENT POLICY (SOC 2-ALIGNED)
Owner: [name / role] · Effective date: [date] · Review cadence: [e.g. annually]
Maps to Trust Services Criteria CC8.1 (change management). Adapt with your auditor and counsel.
1. PURPOSE & SCOPE
This policy governs changes to [systems/repositories in scope] that are authored or
assisted by AI coding tools. It applies to all contributors and to any AI assistant used
to generate, modify, or review production code. It does not change WHAT must be true of a
change (authorized, tested, reviewed, documented) — only clarifies how those controls apply
when AI is the source.
2. AI AS AN AUTHORIZED CONTRIBUTOR
AI coding assistants are permitted contributors within the following scope:
- Allowed: [e.g. drafting code, tests, refactors within approved repositories]
- Not allowed without prior sign-off: [e.g. changes to auth, secrets, infra, data-handling]
AI use is disclosed, not hidden: any change materially authored or assisted by AI is
labeled as such in the change record.
3. HUMAN REVIEW & APPROVAL GATE (the control)
No AI-authored or AI-assisted change reaches production without review and explicit approval
by a qualified human reviewer who is accountable for the change.
- Reviewer must be someone other than the person who prompted the AI, where feasible.
- Approval is recorded (e.g. pull-request approval) and attributable to a named individual.
- The reviewer is responsible for the change as if they had authored it.
Approval throughput must not outpace genuine review — volume is not a reason to relax the gate.
4. TESTING REQUIREMENTS
AI-authored code meets the same testing standard as human-authored code:
- [required automated tests / coverage expectations]
- [security / dependency checks]
The bar does not drop because a machine wrote the code.
5. AUDIT TRAIL & RECORDS
Every change links to: the author (human and/or AI), the reviewer who approved it, the
tests run, and the date. Records are retained for [retention period] and are retrievable for
audit. The trail must answer, for any change: who approved this, and when?
6. ROLES & RESPONSIBILITIES
- Change author: [responsibilities]
- Reviewer / approver: [responsibilities]
- Policy owner: maintains this policy, monitors exceptions, reports to [governance body]
7. EXCEPTIONS & ESCALATION
Emergency or out-of-scope changes require [documented exception process + after-the-fact review].
Exceptions are logged and reviewed at [cadence].
8. REVIEW OF THIS POLICY
This policy is reviewed [cadence] and after any material change to tooling or scope.
--- This is a starting template, not legal or audit advice. Validate against your own SOC 2
scope, your auditor's expectations, and applicable requirements before adopting. ---This template is general education, not legal or audit advice. Validate it against your own SOC 2 scope and your auditor's expectations before adopting.
Want the governance built, not just templated?
A template is the easy part. Making AI-authored code actually pass an audit — wiring the review gate, the audit trail, and the testing into how your team ships — is engineering work. That is what our founder designed under SOC 2 with human-in-the-loop review while leading a 25-plus-person engineering team, and it is what the Fractional AI CTO engagement owns end to end.