A QA status summary can sound authoritative while quietly turning “risk acceptance requested” into “risk accepted.” The person circulating it may have checked the spelling without checking the decision record. An AI usage policy for QA needs to prevent that handoff: it should identify which management tasks AI may assist, who verifies the artifact, and which decisions remain with authorized people.
Keep this policy focused on QA plans, status summaries, risk registers, and decision logs. Agent execution permissions, test-case generation, and tool-specific privacy configuration need separate controls. Permission to draft a management artifact does not grant permission to perform those other activities.
The policy below is a recommended starting point, not a legal requirement or a certification checklist. NIST's voluntary AI Risk Management Framework provides a useful foundation for documented responsibilities and human oversight. Its current Core page presents AI RMF 1.0, published in 2023, and notes that a revision is in progress. The specific task boundaries, reviewer arrangement, and exception rules here are proposed organizational choices.
Copy the bounded template into your policy system and replace the named-role and date fields before seeking approval. It is compact, one-page-ready content; the supporting guide outside the boundary is separate, and actual pagination depends on your document layout.
Policy Template Begins
This policy covers AI assistance with QA plans, status summaries, risk registers, and decision logs. Policy owner: Named QA lead. Effective date: Chosen date. Next policy review: Chosen date. Existing organizational information-handling and approval requirements remain applicable.
Every artifact has a named accountable human owner and a separate qualified human reviewer with access to its supporting records and authority to reject it. The owner remains responsible for its accuracy, completeness, and intended audience.
AI may organize approved inputs, draft or edit narrative, summarize recorded decisions, and propose questions, dependencies, or risks for human consideration. Proposals must remain labeled as proposals.
AI must not invent results, evidence, references, commitments, or approvals; conceal unresolved risks or missing information; change authoritative records; accept risk; approve scope; or authorize release. Unlisted management-artifact tasks require an approved exception before use.
Before circulation or adoption, the reviewer checks material claims against current source records, verifies supplied figures independently of generated prose, checks omissions and contradictions, and confirms that approvals belong to authorized people. Unresolved discrepancies block approval or require an explicitly limited draft that cannot serve as decision evidence.
Retain the artifact revision, source references and cutoff, owner, reviewer, review outcome, and unresolved limitations. Disclose the tool used, which sections or tasks it assisted, and what the human reviewer verified. AI disclosure does not replace review or transfer accountability.
The requester records the exact task and artifact scope, business reason, risks, compensating checks, responsible owner, reviewer, review point, and expiry date, time, and time zone. The policy owner, or a named authorized delegate other than the requester, records approval or rejection before use and any additional required organizational approvals.
Exceptions cannot waive truthful evidence, disclosure, human accountability, or existing approval authority. They expire without automatic renewal. The approver may revoke them immediately; scope or risk changes require reconsideration. Anyone finding a material error must alert the owner, stop reliance on the affected artifact, and initiate correction and notification of recipients.
Policy Template Ends
A useful allowlist names an artifact and the assistance permitted within it. “AI may help QA” leaves too much room for a writer to treat a polished recommendation as an accepted plan. The following distinctions are recommended policy conventions, not claims about controls enforced by a particular application.
Artifact | Permitted Assistance | Human Decision Retained |
|---|---|---|
Status Summary | Draft prose from verified results and a stated cutoff | Confirm evidence and authorize circulation |
QA Plan | Organize approved scope and propose dependencies | Accept scope, assignments, and commitments |
Risk Register | Group recorded risks and suggest questions | Judge impact and accept or reject risk |
Decision Log | Summarize recorded decisions with references | Make approvals and resolve disputed decisions |
For a status summary, the input should identify the release candidate, reporting boundary, and evidence cutoff. The reviewer needs to distinguish an observed result from an inference and a missing result from a successful check. Rewriting the prose must not silently expand the evidence boundary.
For a QA plan, an AI-proposed owner is a suggested assignment until the relevant person accepts it through the team's normal process. Likewise, a proposed dependency date is not a commitment from another team. Preserve those qualifiers when turning planning notes into a stakeholder-friendly document.
ASTQB's guidance on human oversight calls for governance that specifies AI use, reviewers, and coverage decisions. That supports making review responsibilities explicit. It does not establish that copying this template will improve a team's defect rate or that an AI-assisted artifact is reliable because it contains many details.
Naming a reviewer helps only when that person can inspect the evidence, understand the artifact's purpose, and stop an unsupported conclusion. NIST's governance guidance distinguishes responsibilities for using AI from responsibilities for oversight. Apply that principle to the actual document rather than treating a reviewer field as proof that review occurred.
The artifact owner coordinates the work, maintains the current revision, and answers questions from recipients. The reviewer challenges its evidence and interpretation. The decision-maker accepts scope, business risk, or release readiness under existing organizational authority. One review does not manufacture all three decisions.
Choose competence by the claim being checked. A status reviewer needs to understand the selected runs and result definitions. A planning reviewer needs to recognize an unconfirmed dependency. A risk reviewer may need a domain specialist to assess an impact that the author cannot judge. Familiarity with the AI tool alone is insufficient.
For a small team, a separate reviewer might be a lead from a neighboring team or a designated manager with the necessary knowledge and access. If nobody qualified is available, keep the artifact as an unapproved draft or seek an authorized alternative arrangement. Do not make the owner the reviewer merely to complete the record.
Consider a fictional refund-release summary. Its source packet says the refund-risk decision is pending, an older candidate has a passing result, and the current candidate has an unresolved blocker. An illustrative AI-assisted draft says, “Refund testing is complete and the risk is accepted.” This is an authored example, not an observed model response or a real team's report.
The reviewer should reject both conclusions. The earlier pass does not establish the current candidate's outcome, and a pending request does not establish acceptance. A corrected summary should preserve the unresolved blocker and state that the decision remains pending. The authorized risk owner must make any later acceptance decision separately.
Review the source packet as well as the claims the draft happens to include. Otherwise, an omitted blocker can escape a check that only opens the references attached to favorable sentences. Check candidate identity, source revisions, cutoff, missing evidence, and the exact scope of any recorded approval. For a practical source-verification workflow, the Sherlock guide to verifying AI-assisted QA summaries provides a focused companion to this policy.
AI output disclosure should help the next reader understand how an artifact was produced and checked. “AI was used” gives little guidance about whether a model corrected grammar, drafted the risk narrative, or reorganized the entire plan. State the actual contribution without implying that a disclosure label validates the content.
Actual Budget's contributor policy offers a concrete example: it asks contributors to disclose significant AI-generated portions and remains explicit that humans are responsible for what they submit. That is one project's policy, not evidence of how widely QA teams use AI or a rule that automatically applies to your organization.
For QA management artifacts, use a short record containing:
A fictional disclosure might read: “An approved writing assistant helped draft the status narrative in revision R3 from packet REFUND-PACKET-B. Reviewer Maya checked candidate identity, source outcomes, and pending decisions against the retained packet. The dependency confirmation remains unavailable, so this revision is a limited discussion draft and is not approved decision evidence.”
Only record those checks after they occur. Avoid copying restricted prompts or unnecessary confidential details into a broad disclosure record. Retain controlled references where appropriate and follow the organization's existing information-handling policy. The governance record should make verification retrievable without becoming a second uncontrolled copy of the source material.
An exception should answer a specific need that the normal allowlist does not cover. “AI experimentation approved” is too broad: it leaves the artifact, reviewer, decision boundary, and end of permission unclear. Use the exception record to make those limits reviewable before anyone relies on the output.
In a fictional request, QA manager Lena wants AI assistance drafting stakeholder questions and answers for a release meeting, an artifact type outside the template's allowlist. Her proposed record names the release-meeting Q&A only, identifies Lena as owner, and assigns Omar to review every answer against the approved status packet. The stated risk is that a draft answer could imply an unrecorded commitment or risk acceptance.
The requested controls prohibit new commitments, retain supporting references, and label unsupported answers as unresolved. Priya, the designated policy approver who is not the requester, would need to record an actual approval before use. The request proposes a review before circulation and expiry at 17:00 UTC on the meeting date recorded in the request. No approval has occurred in this fictional example.
The completed record needs the actual calendar date as well as the time and time zone. It should also state that Priya can revoke the exception immediately if the source packet changes or a material error appears. After expiry, another Q&A requires a new decision; copying the old approval does not renew permission. The exception cannot turn AI-generated reassurance into evidence or remove the need for human review.
Expiry ends permission for further use under that exception. It does not erase responsibility for artifacts already circulated. If a faulty answer influenced a decision, preserve the reviewed revision, notify affected recipients, correct the record, and ask the responsible decision-maker to reconsider the affected conclusion.
Before adopting the template, connect its roles and records to existing organizational governance. Responsible AI in QA depends on people being able to apply the rules to ordinary work, including when evidence is missing or an exception is inconvenient.
Start with one proposed artifact. Confirm that its AI-assisted task is permitted, its owner and reviewer can explain the evidence boundary, and any exception has a recorded decision and a clear expiry. If those conditions are missing, keep the document out of the approved decision record until the responsible people resolve them.
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