First SOC 2 program
A credible starting point
List approved use cases and place a named reviewer in the workflow before customer communications, security decisions, or external actions are released.
AI / LLM Governance
AI/LLM outputs affecting customer-facing communications, incident decisions, security recommendations, or automated actions require defined human review.
Use this guide to put the control into operation, decide what records to retain, and check that an auditor can trace the evidence back to the work your team performed.
Maintained by GreenHat Security · Reviewed August 21, 2026
AI use is limited to approved purposes, and consequential outputs or actions receive a defined human decision before they affect customers or operations.
First SOC 2 program
List approved use cases and place a named reviewer in the workflow before customer communications, security decisions, or external actions are released.
As the company scales
Use risk-tiered review thresholds, reviewer queues, escalation rules, measured override rates, and quality sampling for high-volume workflows.
Document intended users, allowed decisions, prohibited uses, input data, output destination, and accountable owner before launch.
You should end up with: Signed use-case decision
State which outputs require review, reviewer qualifications, information presented, approval choices, and escalation conditions.
You should end up with: Human-review procedure and decision criteria
Prevent covered output or actions from continuing until the designated reviewer records a decision.
You should end up with: Workflow gate with attributable decision log
Compare workflow activity with approved purposes and review approvals, rejections, overrides, and missed escalations.
You should end up with: Use-case and reviewer performance review
Build the evidence set in three layers: what defines the control, who approved or reviewed it, and what proves it operated. Collect operating records when the work happens so they remain dated, attributable, correctly scoped, and traceable to the underlying activity.
Before sharing, remove unrelated personal or customer data, never expose passwords, tokens, or secret values, preserve enough source context to authenticate the record, and use the secure exchange approved for the engagement.
Documents that define the control, its scope, ownership, and expected way of working.
Confirm what the record proves
Consequential outputs have explicit review triggers, qualified reviewers, decision criteria, escalation routes, and enforced release gates.
Include this context
Covered output or action
Include this context
Trigger
Include this context
Reviewer role
Include this context
Decision criteria
Include this context
Escalation path
Include this context
Enforcement point
Weak evidence to avoid
A statement that humans stay in the loop without defining which outputs, who reviews, or how release is blocked.
Records showing that an accountable person reviewed, approved, challenged, or accepted the work.
Confirm what the record proves
The AI workflow is operating for a defined purpose, audience, data scope, and decision boundary approved by an accountable owner.
Include this context
Use-case ID
Include this context
Workflow ID
Include this context
Allowed and prohibited purposes
Include this context
Data scope
Include this context
Approver
Include this context
Decision and date
Weak evidence to avoid
An email saying the AI feature is approved without its users, data, output destination, or prohibited uses.
Confirm what the record proves
Covered runtime outputs were actually reviewed and resulted in attributable approval, correction, rejection, or escalation before effect.
Include this context
Execution or output ID
Include this context
Workflow version
Include this context
Reviewer
Include this context
Review timestamp
Include this context
Decision
Include this context
Released or corrected result
Weak evidence to avoid
A folder of generated answers with no reviewer identity, decision, timestamp, or indication of what reached the customer.
Type 1
Retain current approved use cases and human-review rules, the latest scheduled annual review and its disposition, plus one recent covered output showing the enforced review and final decision on the selected date.
Type 2
Export every runtime output or proposed action during the period that met a human-review trigger, including approvals, corrections, rejections, escalations, timeouts, cancellations, and any bypass attempt; include every use-case or review-rule change and every scheduled annual use-case and human-review effectiveness review, including no-change completions, missed reviews, and rescheduled reviews.
Reconcile review-queue records to all runtime outputs and actions matching configured triggers, then compare completed approvals with customer-facing messages and executed actions. Separately reconcile every annual review due date from the schedule to a completed no-change or change decision, a documented missed review, or a rescheduled review that preserves the original and new due dates.
Use this checklist to prepare for procedures an auditor may perform. The exact steps and sample selection depend on your engagement scope and the service auditor's professional judgment.
Determine whether the control is designed to achieve this result: AI use is limited to approved purposes, and consequential outputs or actions receive a defined human decision before they affect customers or operations.
Compare the documented owner with the intended role (AI Workflow Owner / CISO / Engineering), then compare dated records with the stated cadence: Per workflow/change; review at least annually.
Reconcile review-queue records to all runtime outputs and actions matching configured triggers, then compare completed approvals with customer-facing messages and executed actions. Separately reconcile every annual review due date from the schedule to a completed no-change or change decision, a documented missed review, or a rescheduled review that preserves the original and new due dates.
Retain current approved use cases and human-review rules, the latest scheduled annual review and its disposition, plus one recent covered output showing the enforced review and final decision on the selected date.
Export every runtime output or proposed action during the period that met a human-review trigger, including approvals, corrections, rejections, escalations, timeouts, cancellations, and any bypass attempt; include every use-case or review-rule change and every scheduled annual use-case and human-review effectiveness review, including no-change completions, missed reviews, and rescheduled reviews.
Documents that define the control, its scope, ownership, and expected way of working.
For each selected record, confirm it demonstrates Consequential outputs have explicit review triggers, qualified reviewers, decision criteria, escalation routes, and enforced release gates.
Records showing that an accountable person reviewed, approved, challenged, or accepted the work.
For each selected record, confirm it demonstrates The AI workflow is operating for a defined purpose, audience, data scope, and decision boundary approved by an accountable owner.
For each selected record, confirm it demonstrates Covered runtime outputs were actually reviewed and resulted in attributable approval, correction, rejection, or escalation before effect.
Use the categories that apply to this control: connect any policy or design artifact to its approval or review record, then trace a selected operating record through execution, result, and any exception or remediation.
These identifiers help you navigate related Trust Services Criteria. They do not reproduce the criteria or prove that this control fully addresses them in your environment.
Confirm final scope, mappings, and testing expectations with your service auditor. SOC 2® is an AICPA trademark; GreenHat Security is not affiliated with or endorsed by AICPA.