GOVERN-1.4: The risk management process and its outcomes are established through transparent policies, procedures, and other controls based on organizational risk priorities.

To meet govern-1.4: the risk management process and its outcomes are established through transparent policies, procedures, and other controls based on organizational risk priorities. requirement, you must document an AI risk management process that is understandable to stakeholders, tied to your risk priorities, consistently executed, and provable with retained evidence. Treat this as an operating control: clear ownership, defined cadence/trigger events, and traceable outcomes. ¹

Key takeaways:

• Publish a risk management policy plus procedures that show “who does what, when, using what inputs, producing what outputs.”
• Map risk priorities to concrete controls and decision points (approve, mitigate, accept, stop, monitor) for each AI use case.
• Standardize the evidence bundle so you can prove both the process and the resulting decisions over time. ²

GOVERN-1.4 is an execution requirement disguised as a governance statement. The work is not writing a policy; it is making your AI risk management process legible, repeatable, and auditable across the AI lifecycle, then showing the outcomes of that process (approvals, risk acceptances, mitigations, monitoring decisions) with consistent records. NIST’s intent is straightforward: risk management must be anchored in organizational priorities and implemented through transparent policies, procedures, and controls, not ad hoc judgments or private Slack threads. ¹

For a CCO, GRC lead, or Compliance Officer, operationalizing this requirement means converting “AI risk management” into a set of runbooks and control checks that product, engineering, data science, security, privacy, and legal can actually follow. Transparency here means a third party, auditor, or internal reviewer can reconstruct what you did, why you did it, and who approved it, without relying on personal memory. ²

If you do one thing: build a single, shared “AI Risk Management Playbook” that ties your risk priorities to required assessments, gating decisions, and minimum evidence for each AI system type and deployment pattern.

Regulatory text

Text (excerpt): “The risk management process and its outcomes are established through transparent policies, procedures, and other controls based on organizational risk priorities.” ¹

What the operator must do:
You must (1) define and publish how AI risk is managed at your organization, (2) implement that definition as procedures and controls that teams follow, and (3) produce outcomes (decisions and actions) that can be traced back to risk priorities and shown through retained evidence. “Transparent” means a reviewer can see the rules, the steps, the owners, and the results. ²

Plain-English interpretation (what this requirement really asks)

• Have a documented process for identifying, assessing, treating, and monitoring AI risks.
• Make it understandable and accessible to the people who must execute it and the people who oversee it (business owners, risk, compliance).
• Tie it to your risk priorities so the process is not generic. Your priorities should change thresholds, gating, and required mitigations.
• Prove the outcomes with records: what risks were identified, what decisions were made, what controls were selected, and what monitoring was required. ¹

A practical test: if your CCO asked, “Show me how we decide whether an AI feature can ship,” your team should produce a short, consistent packet that includes the policy basis, the executed procedure steps, and the decision record.

Who it applies to (entity and operational context)

This applies to any organization that develops, deploys, or operates AI systems, including service organizations supporting AI-enabled services. ¹

Operationally, it applies wherever AI risk decisions happen, including:

• Product delivery: new models, model updates, new features using third-party models, changes in intended use.
• Data lifecycle: training data sourcing, labeling, retention, and access decisions that affect risk.
• Third-party risk: model providers, data providers, evaluation vendors, and hosted AI platforms.
• Business use: customer-facing decisions, internal automation, HR, fraud, or safety-related use cases.

If your organization has multiple lines of business, you can centralize the policy and standardize the minimum controls, then allow procedures to vary by risk tier.

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

1) Define “organizational risk priorities” in operational terms

Create a short set of prioritized AI risk themes that matter to your business, written as decision drivers. Examples (customize to your reality):

• Safety and harmful output risk
• Privacy and data protection risk
• Security and abuse resistance
• Bias/fairness and impact to protected or sensitive groups
• Reliability and performance in intended context
• Legal/contractual constraints and customer commitments

Then translate priorities into rules: what requires escalation, what requires pre-release testing, what requires ongoing monitoring, and what is prohibited.

Artifact: AI Risk Priorities Statement (approved by senior leadership). ²

2) Write a policy that sets the “non-negotiables”

Your AI Risk Management Policy should specify:

• Scope (what counts as an AI system in your environment)
• Roles and decision rights (Product Owner, Model Owner, Risk/Compliance, Security, Privacy, Legal)
• Required risk activities (intake, assessment, treatment, monitoring, incident response linkage)
• Risk acceptance authority and documentation requirements
• Exceptions process

Keep the policy stable. Put the operational detail in procedures so teams can execute and you can update quickly.

Artifact: AI Risk Management Policy (versioned, approved). ¹

3) Create procedures that map to lifecycle triggers

Define procedures that start when something changes. Common trigger events:

• New AI use case intake
• New model training or fine-tuning
• Material model update
• New data source or new sensitive data category
• Third-party model or platform change
• Expanded intended use or new user population

For each trigger, document:

• Required inputs (model card or system description, data sources, intended use, user impact)
• Required assessments (privacy, security, bias/fairness where relevant, performance testing)
• Required approvals (who signs off)
• Required outputs (risk register updates, mitigation plan, go/no-go decision)

Artifact: AI Risk Procedures ² plus a simple intake form. ²

4) Convert procedures into controls with owners and cadence

This is where most programs fail: they stop at “we have a policy.” Build controls that can be tested.

Use three control patterns (recommended):

1. Requirement control card per control: objective, owner, trigger events, execution steps, exception rules. ²
2. Minimum evidence bundle per execution cycle: inputs, approvals, output artifacts, and retention location. ²
3. Control health checks: recurring checks and tracked remediation to validated closure with due dates. ²

If you use Daydream, implement these as control records tied to systems and third parties so the evidence packet is generated consistently during audits and customer diligence.

5) Standardize “outcomes” so they are inspectable

Define what counts as an outcome and require it to be recorded:

• Risk tier assignment (with rationale)
• Key identified risks and inherent risk rating
• Selected mitigations and residual risk rating
• Approval decision (approve, approve with conditions, reject, defer)
• Monitoring requirements and metrics owner
• Risk acceptance record when residual risk remains

Artifact: AI Risk Decision Record template and a central AI risk register. ¹

6) Build transparency into reporting

Transparency is not only documentation; it is visibility.

• Provide leadership reporting: risk themes, open issues, accepted risks, overdue mitigations.
• Provide delivery-team reporting: required actions before release, monitoring tasks after release.
• Provide audit-ready traceability: link policy → procedure → control → evidence → outcome. ²

Required evidence and artifacts to retain

A reviewer should be able to reconstruct “policy to proof” for any AI system. Retain:

• Approved AI Risk Management Policy (version history, approval record) ¹
• AI risk procedures/runbooks tied to trigger events ²
• Control cards with owners and exception rules ²
• Completed AI intake forms and scoping documents
• Risk assessments and test results (as applicable to your environment)
• Risk decision records (approvals, conditions, and acceptances)
• Evidence of monitoring setup and periodic reviews
• Control health check logs and remediation tickets through closure ²
• Exception requests and documented approvals

Retention period is not specified by NIST AI RMF; set one aligned to your enterprise retention policy and be consistent. ²

Common exam/audit questions and hangups

Expect these lines of questioning:

• “Show me the end-to-end process from intake to approval to monitoring for one AI system.” ¹
• “How do your organizational risk priorities change what you do in practice?”
• “Who can accept risk, and where is that documented?”
• “How do you know controls are operating consistently across teams?” ²
• “How do third-party models and data providers enter this workflow?”

Hangups that slow audits:

• Policy exists, but procedures are tribal knowledge.
• Evidence is scattered across email, tickets, and docs with no consistent bundle.
• Risk acceptance decisions are undocumented or made by people without authority.

Frequent implementation mistakes (and how to avoid them)

1. Mistake: One generic AI policy for everything.
   Fix: Define risk tiers and tie them to specific gating steps and evidence.

2. Mistake: “Transparency” interpreted as publishing a long document.
   Fix: Publish short policy + executable procedures + templates. Make outcomes queryable in a register. ²

3. Mistake: No trigger events.
   Fix: Write procedures around changes (new model, new data, new intended use). Trigger-based controls prevent drift.

4. Mistake: No exception path.
   Fix: Create an exception workflow with time bounds, compensating controls, and named approvers.

5. Mistake: Health checks are informal.
   Fix: Schedule control health checks and track remediation to closure with evidence. ²

Enforcement context and risk implications

NIST AI RMF is a framework, not a penalty-bearing regulation by itself. Your exposure usually shows up indirectly: customer audits, procurement requirements, contractual representations, and sector regulators expecting disciplined risk management practices. A non-transparent process raises a predictable failure mode: you cannot explain or defend decisions after an incident, model failure, or third-party issue. ²

Practical 30/60/90-day execution plan

First 30 days (stand up the operating skeleton)

• Name owners: executive sponsor, AI risk process owner, and approval authorities.
• Draft and approve AI Risk Priorities Statement.
• Publish AI Risk Management Policy v1 (scope, roles, decision rights, exceptions). ¹
• Build templates: intake form, decision record, risk acceptance record.
• Pilot on one high-visibility AI system to prove the workflow.

Days 31–60 (convert policy into procedures and controls)

• Write trigger-based procedures for: new AI use case, material update, new data source, third-party model onboarding.
• Create control cards and define the minimum evidence bundle per procedure. ²
• Stand up a central AI risk register and link each system to its evidence location.
• Train product/engineering on “how to ship with the process,” not on policy reading.

Days 61–90 (make it durable and auditable)

• Run the first control health check cycle and open remediation items with accountable owners. ²
• Start leadership reporting: accepted risks, overdue mitigations, systems lacking evidence.
• Expand coverage: onboard remaining AI systems and key third parties into the same evidence and outcome structure.
• Pressure-test transparency: pick a system and attempt to reconstruct its decisions in under an hour using only retained artifacts.

Frequently Asked Questions

Do we need a separate AI risk policy if we already have enterprise risk management (ERM)?

You can keep ERM as the umbrella, but you still need AI-specific procedures and controls that translate risk priorities into AI lifecycle decisions and evidence. GOV-1.4 expects transparency in how AI risk management is executed, not only high-level governance. ¹

What does “transparent” mean in practice for audits and customer diligence?

A reviewer can follow a documented trail: policy and procedures, the performed steps, and a decision record showing approvals and conditions. If outcomes live only in meetings or chat, transparency fails. ²

How do we tie “organizational risk priorities” to actual go/no-go decisions?

Convert priorities into explicit gates and thresholds by risk tier, then require a decision record that cites which priority drove the mitigation or restriction. Document who can accept residual risk and where that acceptance is stored. ¹

We use third-party foundation models. Are we still on the hook for this requirement?

Yes. You still need a transparent process for onboarding the third party, evaluating fit for intended use, documenting risks, and recording approvals and monitoring requirements. Treat the model provider as part of your risk control environment. ²

What is the minimum evidence bundle we should keep per AI release?

Keep the intake/scoping record, risk assessment outputs that your procedures require, the approval decision with conditions, the risk acceptance (if applicable), and proof that monitoring was set up. Standardize the storage location so you can retrieve it quickly. ²

How can Daydream help without turning this into a tool-first exercise?

Use Daydream to implement control cards, assign owners, automate evidence collection pointers, and run control health checks with tracked remediation. The policy and procedures remain yours; the system keeps execution and evidence consistent. ²

Footnotes

1. NIST AI RMF Core

2. NIST AI RMF 1.0