GOVERN-4.2: Organizational teams document the risks and potential impacts of the AI technology they design, develop, deploy, evaluate, and use, and they communicate about the impacts more broadly.

GOVERN-4.2 sits in the “GOVERN” function of the NIST AI Risk Management Framework and forces a practical outcome: you cannot manage AI risk you have not documented, and you cannot claim responsible AI governance if impacts stay trapped inside the product team. The requirement is explicitly lifecycle-wide: it covers AI you build, AI you buy, AI you embed, and AI you operate day-to-day. It also covers evaluation activities (testing, validation, red-teaming) because those steps often surface issues that never make it into durable governance records.

For a Compliance Officer, CCO, or GRC lead, the fastest path is to turn this into a control with (1) a standard artifact, (2) clear ownership, (3) lifecycle checkpoints where the artifact must be created/updated, and (4) defined communications that reach legal, privacy, security, business leadership, and other affected stakeholders. Done well, GOVERN-4.2 becomes your evidence backbone: it links impact identification to mitigations, residual risk decisions, and the ongoing monitoring you will need if the model drifts or the use case expands. ¹

Regulatory text

Text (excerpt): “Organizational teams document the risks and potential impacts of the AI technology they design, develop, deploy, evaluate, and use, and they communicate about the impacts more broadly.” ²

What the operator must do:

• Produce written documentation that captures risks and potential impacts for each AI system across its lifecycle (design, development, deployment, evaluation, and use). ²
• Communicate those impacts beyond the immediate team in a structured way (for example: internal governance forums, impacted business owners, and other relevant stakeholders). ²

This is a governance and accountability control. If your AI program has risk assessments but no durable, reviewable record that survives personnel changes, model updates, and audits, you are not meeting the intent of GOVERN-4.2. ²

Plain-English interpretation (what “good” looks like)

GOVERN-4.2 means every AI system needs a living “paper trail” that answers:

1. What could go wrong (risks)?
2. Who or what could be affected (impacts)?
3. How you know (evaluation results)?
4. What you decided to do about it (controls and acceptance)?
5. Who you told (communications), and what you told them? ²

“Communicate more broadly” should not be left to chance. Define what is shareable, with whom, at what level of detail, and how you handle sensitive content (security, IP, privacy, and contractual constraints).

Who it applies to (entity and operational context)

Applies to: organizations that develop or deploy AI systems, including organizations that only “use” AI (for example, embedding third-party AI in customer workflows). ²

Operational scope you should assume:

• Internally built ML models and GenAI apps.
• Third-party AI models, APIs, and platforms (including copilots, fraud tools, HR screening, marketing decisioning).
• “Shadow AI” that is later discovered and brought into governance.

Teams implicated:

• Product and engineering (system design and change control)
• Data science/ML engineering (model development and evaluation)
• Security and privacy (abuse cases, data handling, monitoring)
• Legal and compliance (risk acceptance, disclosures, claims substantiation)
• Procurement/TPRM (third-party AI risk and contractual controls)
• Customer support/operations (real-world impact signals, complaints)

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

Step 1: Define the standard artifact (your “AI Risk & Impact Record”)

Create a template that is required per AI system and versioned. Minimum fields that auditors and governance bodies expect to see map cleanly to the requirement language:

AI Risk & Impact Record (minimum sections)

• System identification: name, owner, business purpose, lifecycle status
• Model and dependency overview: model type, data sources, third-party components
• Intended use and prohibited use
• Impact mapping: who is affected (users, customers, employees, non-users), impact types (financial, safety, privacy, civil rights, reputational, operational)
• Risk register: risks, likelihood/severity criteria (your internal scale), inherent vs residual risk
• Evaluation summary: what tests were run and what failed (bias/fairness checks, robustness, security testing, performance metrics, red-team findings)
• Mitigations and controls: guardrails, human review, monitoring, fallback procedures
• Residual risk decision: approver, date, conditions, re-review triggers
• Communications log: audiences, materials shared, dates, approvals, feedback received
• Change log: model updates, prompt changes, feature expansions, data changes

This converts GOVERN-4.2 into something inspectable and repeatable. ²

Step 2: Assign ownership and RACI

Document a control owner (often GRC or Responsible AI lead) and required contributors (product, data science, security, privacy, legal). Require an accountable business executive to sign off on residual risk for high-impact use cases.

A practical RACI:

• Accountable: Business owner of the AI use case
• Responsible: Product/ML owner for completing the record
• Consulted: Privacy, security, legal, TPRM
• Informed: Internal audit, customer-facing teams, executive risk committee (as defined)

Step 3: Tie documentation to lifecycle gates

You need mandatory checkpoints where the record is created or refreshed:

• Design intake: initial risks/impacts, intended use, data sources
• Pre-deployment: evaluation results, mitigations, sign-off, comms plan
• Post-deployment: monitoring plan, incident response hooks, user feedback signals
• Change events: retraining, model swap, new data sources, new geography, new decision use

If you run an SDLC, map this to your existing change management approvals so teams cannot release without an updated record. ²

Step 4: Make “communicate more broadly” operational

Define communication tiers so teams know what to share and where.

Minimum communication set (internal):

• Governance forum: periodic review of top AI risks and impact themes
• Impacted stakeholders: business process owners, frontline operations, support
• Control functions: legal, privacy, security, compliance, internal audit
• Third-party oversight: procurement/TPRM for systems with external dependencies

Outputs you can standardize:

• One-page “AI Impact Summary” derived from the record (sanitized for broad distribution)
• Release notes for model changes that affect user outcomes or controls
• Internal FAQ for support and sales about limitations and expected behavior

For sensitive details (for example, security abuse cases), store full detail in a restricted annex and communicate a redacted summary more broadly. That still satisfies the “communicate” intent while controlling exposure.

Step 5: Evidence the control is operating (not just designed)

Run a recurring control test:

• Sample AI systems in production and confirm a current Risk & Impact Record exists.
• Confirm the communications log contains dated artifacts and approvals.
• Verify evaluation results are linked (or attached) and reflect the deployed version.

If you use Daydream for control mapping and evidence collection, treat GOVERN-4.2 as a control with an owner, a checklist of required artifacts, and an evidence schedule so collection is continuous instead of audit-time scrambling.

Required evidence and artifacts to retain

Retain artifacts in a system of record with version control:

• AI system inventory entry tied to owner and status
• AI Risk & Impact Record (current and prior versions)
• Evaluation reports and test outputs referenced in the record
• Residual risk sign-off (approval workflow, meeting minutes, or ticket)
• Communications artifacts: emails, memos, decks, governance minutes, internal posts
• Change management tickets tying model releases to record updates
• Exceptions and remediation plans when documentation is incomplete

Common exam/audit questions and hangups

Auditors and regulators tend to probe for traceability and completeness:

• “Show me the documented impacts for this AI system and who approved residual risk.”
• “How do you ensure records are updated after model changes?”
• “Where is the evidence you communicated impacts outside the project team?”
• “How do you handle third-party AI where you do not control the model?”
• “How do complaints/incidents feed back into your risk and impact documentation?”

Hangup to expect: teams will show a slide deck. You need a controlled artifact with owners, dates, and change history. ²

Frequent implementation mistakes (and how to avoid them)

1. One enterprise-level statement instead of per-system documentation.
   Fix: require a record per AI system and per material version change.

2. Only technical risks; missing human, legal, and operational impacts.
   Fix: force an impact taxonomy section (privacy, fairness, accessibility, safety, financial harm, reputational harm).

3. No communication evidence.
   Fix: require a communications log with attachments and approvals as part of the record.

4. Documentation created once, then abandoned.
   Fix: bind updates to change control and post-release monitoring events.

5. Third-party AI treated as “out of scope.”
   Fix: document what you can: intended use, dependency risks, contractual controls, and compensating monitoring.

Enforcement context and risk implications (without over-claiming)

No public enforcement cases are provided in the source catalog for this specific NIST AI RMF requirement. GOVERN-4.2 is still high-value defensibility: when an AI incident occurs, the first questions are what you expected could happen, what tests you ran, what you changed, and who you informed. Poor documentation and weak communications increase regulatory, litigation, and reputational exposure because you cannot substantiate governance claims or show accountable decision-making. ²

Practical 30/60/90-day execution plan

First 30 days (foundation)

• Name the control owner and approve a RACI.
• Publish the AI Risk & Impact Record template and minimum completion standards.
• Identify your in-scope AI inventory baseline (even if incomplete) and select priority systems for first-pass documentation.
• Define communication tiers and what must be logged as evidence. ²

Days 31–60 (implement in workflows)

• Embed the record into SDLC/change management: no deploy without an updated record and sign-off for defined risk levels.
• Train product, data science, security, and privacy reviewers on how to complete and challenge entries.
• Create a standard “AI Impact Summary” output for broader internal distribution and add it to release governance.

Days 61–90 (operate and test)

• Run a control effectiveness check: sample systems, verify completeness, verify communications evidence, verify linkage to evaluation results.
• Close gaps with remediation tickets and deadlines owned by product teams.
• Establish ongoing governance reporting: top risks, recurring impacts, open issues, exceptions, and upcoming reviews.

Frequently Asked Questions

Does GOVERN-4.2 require public disclosure of AI risks?

The text requires teams to “communicate about the impacts more broadly,” but it does not specify public disclosure. Implement defined internal audiences and, where appropriate, external communications governed by legal, privacy, and security review. ²

We buy an AI tool from a third party. What do we document if we can’t see the model?

Document the intended use, known limitations, dependency risks, your evaluation results in your environment, and your compensating controls (monitoring, human review, escalation). Also document what you requested contractually and what you received. ²

What counts as “communicate” for audit purposes?

A defensible approach includes a defined channel (governance committee, risk forum, documented review) and retained artifacts (minutes, memos, approvals). Verbal updates without records are hard to evidence.

How granular should “potential impacts” be?

Start with impact categories and affected stakeholder groups, then go deeper where the use case is high-impact or customer-facing. Your record should be detailed enough that a reviewer can understand plausible harm paths and the controls selected.

Can we reuse an enterprise risk assessment across multiple AI systems?

You can reuse common risk statements and control libraries, but each AI system still needs a system-specific record: context, data, evaluation results, and communications evidence differ by use case. ²

Who should approve residual AI risk?

Assign approval to the accountable business owner with consultation from legal, privacy, security, and compliance. Document the conditions of approval and the triggers for re-review. ²

Footnotes

1. NIST AI RMF Core; Source: NIST AI RMF program page

2. NIST AI RMF Core