GOVERN-5.2: Mechanisms are established to enable the team that developed or deployed AI systems to regularly incorporate adjudicated feedback from relevant AI actors into system design and implementation.

GOVERN-5.2 is a governance requirement about operational discipline: you must have mechanisms that let the team that developed or deployed an AI system regularly incorporate adjudicated feedback from relevant AI actors into system design and implementation (NIST AI RMF Core). The phrase “adjudicated feedback” is the fulcrum. It means feedback is not automatically accepted, ignored, or left to informal debate. It is reviewed, validated, categorized, assigned an owner, and resolved with a recorded decision.

For a Compliance Officer, CCO, or GRC lead, the fastest path is to treat this like a specialized issue-management and change-management control: define feedback sources, standardize intake, run a triage/adjudication workflow, connect outcomes to engineering change control, and collect evidence every cycle. You are proving governance and traceability, not promising perfection in model behavior.

This page gives requirement-level implementation guidance you can deploy quickly: applicability, step-by-step execution, evidence to retain, typical audit traps, and a practical 30/60/90-day rollout plan. Primary reference is the NIST AI RMF Core and the NIST AI RMF program page (NIST AI RMF Core; NIST AI RMF program page).

Regulatory text

Requirement (verbatim): “Mechanisms are established to enable the team that developed or deployed AI systems to regularly incorporate adjudicated feedback from relevant AI actors into system design and implementation.” (NIST AI RMF Core)

Operator meaning: You must implement (1) defined channels to gather feedback from relevant parties, (2) a documented adjudication process to decide what to do with that feedback, and (3) a controlled path to incorporate approved feedback into design and implementation, with repeatable frequency and evidence (NIST AI RMF Core).

Plain-English interpretation (what the requirement is really asking)

GOVERN-5.2 expects a closed-loop governance mechanism:

1. Feedback is collected from people who build, operate, are impacted by, or depend on the AI system.
2. Feedback is adjudicated (triaged, validated, risk-ranked, and decided) by accountable roles, using defined criteria.
3. Decisions become changes: model updates, prompt/guardrail changes, documentation updates, UI changes, monitoring thresholds, or policy constraints.
4. This happens regularly, not just after major incidents.

A program that only accepts bug reports is incomplete. A program that only holds a monthly meeting without change tickets is also incomplete. You need both: governance decisions and technical execution, tied together.

Who it applies to (entity and operational context)

Applies to: Any organization that develops or deploys AI systems (NIST AI RMF Core).

Operational contexts where auditors will expect this control:

• Customer-facing AI (chatbots, recommendations, eligibility/underwriting support, fraud flags).
• Employee-facing AI (HR screening support, internal copilots, call-center assist).
• Safety- or rights-impacting AI (health, finance, housing, employment-related workflows).
• AI supplied by a third party where you configure, fine-tune, integrate, or make decisions based on outputs. You still need a feedback mechanism, even if you cannot change the base model.

Relevant AI actors (define per system):

• End users and operators (front-line staff).
• Impacted individuals/groups (customers, applicants, employees).
• System owners (product), developers (ML/engineering), and deployers (IT/ops).
• Risk partners (legal, compliance, privacy, security, model risk).
• Third parties (model/API providers, system integrators) where their components affect behavior.

What you actually need to do (step-by-step)

1) Assign ownership and governance hooks

• Name a control owner for GOVERN-5.2 (usually AI governance lead, model risk, or product risk).
• Define decision authority: who can accept a change request, who can reject, and who can override.
• Integrate with existing forums: change advisory board (CAB), model risk committee, privacy review, incident review. Avoid creating a parallel process unless necessary.

Deliverable: RACI for feedback intake, adjudication, and implementation.

2) Define “feedback” and “adjudication” criteria for your environment

Create a one-page standard that answers:

• What counts as feedback: defects, harmful outputs, bias claims, usability issues, safety concerns, monitoring alerts, red-team findings.
• Required fields: system name/version, context, input/output, impact, reporter type, severity, evidence attachment.
• Adjudication criteria: user harm potential, policy/regulatory impact, reproducibility, frequency, materiality, security/privacy implications, contractual obligations with third parties.

Deliverable: Feedback adjudication standard operating procedure (SOP) mapped to GOVERN-5.2 (NIST AI RMF Core).

3) Implement intake mechanisms (multiple channels, one system of record)

Pick channels that match your risk profile, then converge them into a single queue:

• In-product reporting (thumbs down + reason + transcript capture).
• Internal ticketing (engineering/Jira, ITSM/ServiceNow).
• Trust/compliance intake (ethics hotline variant for AI issues, privacy inbox).
• Customer support escalations tagged “AI”.
• Monitoring alerts (drift, toxicity, hallucination flags) routed as “system feedback”.

Key control point: every channel must land in the same adjudication workflow, even if submitted elsewhere.

Deliverable: Intake map + system-of-record configuration.

4) Run adjudication (triage) on a defined cadence

Set a cadence appropriate to system risk and change frequency. Your procedure should include:

• Initial triage: duplicate detection, severity assignment, data sensitivity classification.
• Validation: reproduce output; confirm prompt/context; confirm whether issue is expected behavior.
• Decision: accept as change request, defer, reject with rationale, treat as incident, or route to third party.
• Assignment and due dates: every accepted item gets an owner and tracked remediation path.

A practical pattern: a weekly or biweekly triage meeting for high-volume systems, plus ad hoc escalation for severe events. Document the cadence in your SOP as a control requirement (NIST AI RMF Core).

Deliverable: Triage log with decisions and sign-off.

5) Convert adjudicated feedback into controlled change

For accepted feedback, force it into your standard delivery pipelines:

• Design change: update requirements, risk controls, UX warnings, model cards, acceptable-use constraints.
• Implementation change: retraining plan, fine-tune, prompt template update, policy/guardrail changes, retrieval corpus update, rate limits.
• Test change: add regression tests reflecting the issue (e.g., a “known bad prompt” test).
• Release change: versioning, release notes, rollback plan, stakeholder notification.

Key control point: link each feedback item to a change ticket and to the release or configuration change that resolved it.

Deliverable: Traceability from feedback ID → decision record → change ticket → release artifact.

6) Close the loop with the reporter and stakeholders

Closure is part of “mechanisms”:

• Confirm receipt and status (where appropriate).
• Communicate outcomes for high-impact issues to risk owners and operations.
• If a third party is involved, document escalation, vendor response, and your compensating controls.

Deliverable: Closure notes + stakeholder notifications (as applicable).

7) Measure control operation (lightweight metrics, no vanity dashboards)

Track a small set of governance metrics to prove regular operation:

• Volume by category and severity.
• Decision outcomes (accept/reject/defer) with rationale quality checks.
• Aging of accepted items.
• Repeat issues that indicate systemic gaps (e.g., missing tests or unclear use policy).

Keep metrics descriptive. Avoid numeric performance claims unless you can support them with your own records.

Deliverable: Monthly/quarterly governance report pack.

Required evidence and artifacts to retain (audit-ready checklist)

Retain evidence per AI system (or per system family) in a central GRC repository:

• Policy/SOP for feedback intake and adjudication mapped to GOVERN-5.2 (NIST AI RMF Core).
• RACI / role assignments and committee charters (if used).
• Inventory of relevant AI actors and feedback channels per system.
• System-of-record extracts: tickets, forms, user reports, monitoring alerts.
• Adjudication records: meeting minutes, triage logs, decision notes, risk acceptance approvals.
• Change artifacts: linked engineering tickets, PRs, model/prompt versions, configuration change records.
• Test artifacts: added regression tests, evaluation results tied to feedback themes.
• Release notes and approvals showing incorporation into design/implementation.
• Third-party escalations and responses where the AI component is external.

Common exam/audit questions and hangups

• “Show me the mechanism.” Expect a walkthrough from intake to closure with real samples.
• “Who are relevant AI actors for this system, and why?” If you cannot justify inclusions/exclusions, you will look arbitrary.
• “What does adjudicated mean here?” Auditors will look for documented criteria, accountable approvers, and recorded rationale.
• “Prove regular incorporation.” They will ask for multiple cycles of evidence: recurring triage, recurring changes, recurring reporting.
• “What happens when a third party owns the model?” You still need intake, adjudication, escalation, and compensating controls.

Frequent implementation mistakes (and how to avoid them)

1. Mistake: Treating feedback as customer support only.
   Fix: Route support issues into a risk triage workflow when they implicate model behavior, fairness, safety, or policy.

2. Mistake: No definition of “adjudication.”
   Fix: Write explicit decision categories and acceptance criteria, require rationale, and assign an approver role (NIST AI RMF Core).

3. Mistake: Decisions don’t translate into engineering work.
   Fix: Enforce ticket linkage to code/config changes and releases. No linkage, no closure.

4. Mistake: Only collecting feedback after incidents.
   Fix: Stand up continuous channels (in-product, internal ops, monitoring alerts) and keep the cadence even during “quiet” periods.

5. Mistake: Ignoring feedback from impacted parties.
   Fix: Identify impacted groups and provide accessible reporting paths; record how you considered their feedback in adjudication.

Enforcement context and risk implications

The NIST AI RMF is a framework, so it is not itself a penalty-bearing regulation (NIST AI RMF program page). The risk is practical: weak feedback loops produce repeated harmful outcomes, inconsistent decisions, and poor traceability. If an external regulator, customer, or litigant asks what you did after receiving credible complaints, GOVERN-5.2 is the governance proof point: you can show intake, decisioning, and design changes with timestamps and approvals (NIST AI RMF Core).

A practical 30/60/90-day execution plan

First 30 days (stand up the minimum viable control)

• Pick one high-impact AI system as the pilot.
• Assign the control owner and define adjudication roles (product, engineering, compliance/risk).
• Create the feedback intake form and tag schema; route all channels to one queue.
• Write the adjudication SOP and decision categories; schedule recurring triage.
• Start collecting evidence from day one (tickets + meeting notes).

By 60 days (connect to change management and testing)

• Require every accepted feedback item to map to a change ticket and a release artifact.
• Add a regression test pattern: “feedback-derived test cases” become mandatory for accepted issues.
• Build a short governance report pack (themes, decisions, open items).
• Extend the mechanism to additional relevant AI actors (operations, customer support, impacted-user intake).

By 90 days (scale and harden)

• Expand across the AI inventory based on risk tiering.
• Formalize third-party escalation playbooks and SLAs where contracts permit.
• Add periodic quality checks: sample adjudications for rationale quality and consistency.
• If you use Daydream, map GOVERN-5.2 to the control owner, procedure, and recurring evidence collection so you can produce an audit packet quickly and consistently across systems (NIST AI RMF Core).

Frequently Asked Questions

What counts as “adjudicated feedback” in practice?

Feedback is adjudicated when you can show a documented triage/decision step with criteria, an accountable decision-maker, and a recorded outcome (accept/reject/defer/escalate) tied to a tracked remediation path (NIST AI RMF Core).

Do we need to incorporate every piece of feedback?

No. You need a mechanism to regularly incorporate adjudicated feedback, which includes the ability to reject or defer with documented rationale and risk acceptance where appropriate (NIST AI RMF Core).

If the AI model is provided by a third party, can we still meet this requirement?

Yes, if you collect feedback, adjudicate it, and either implement compensating changes in your layer (prompts, guardrails, UI, policy, monitoring) or escalate to the third party with documented outcomes.

Who are “relevant AI actors” for an internal-only AI assistant?

Typically: employees using it, the business owner, IT/operations, security, privacy, compliance, and any third party provider whose component affects outputs. Document the list per system and review it when the use case changes.

How do we prove “regularly” without a prescribed cadence?

Define your cadence in the SOP (based on system risk and change rate), follow it, and retain recurring evidence: triage logs, decision records, and linked releases over time (NIST AI RMF Core).

What evidence is most persuasive to an auditor?

Traceability: a feedback item with an ID, an adjudication decision with rationale, and a linked change ticket and release artifact showing the design/implementation update (NIST AI RMF Core).