GOVERN-2.3: Executive leadership of the organization takes responsibility for decisions about risks associated with AI system development and deployment.

To meet govern-2.3: executive leadership of the organization takes responsibility for decisions about risks associated with ai system development and deployment. requirement, you must assign an executive owner for AI risk decisions and prove that leadership reviews, approves, and accepts (or rejects) AI risk at defined gates before deployment. Document decision authority, escalation paths, and the evidence trail for each material AI risk decision. ¹

Key takeaways:

• Executive leadership must be the accountable decision-maker for AI risk acceptance, not just informed after the fact. ¹
• Operationalize with clear decision gates, defined risk appetite, and a repeatable approval workflow tied to AI system releases. ²
• Evidence matters: meeting minutes, sign-offs, risk memos, and exception records must be retained and traceable to each AI system. ²

GOVERN-2.3 is a governance control with a simple test: can you show that an executive made (or explicitly delegated) the key decisions about AI risk for development and deployment, and that those decisions were based on an understandable risk record? The requirement is not satisfied by a policy that says “leadership supports responsible AI.” It is satisfied when leadership can be named, their decision rights are defined, and their approvals are consistently captured at the moments that matter, such as model launch, major model changes, new use cases, and high-severity incidents. ¹

For a Compliance Officer, CCO, or GRC lead, the fastest path is to treat this as a “decision governance” control: define who decides, what they decide, when they decide, and what evidence proves the decision occurred. Then wire those decision points into existing operating rhythms (product release, change management, incident response, third-party onboarding) so the control runs without heroics. ²

This page gives requirement-level implementation guidance you can put into production: a decision matrix, step-by-step workflow, minimum evidence bundle, common audit questions, and a practical execution plan you can run with your existing governance structure. ²

Regulatory text

Text (excerpt): “Executive leadership of the organization takes responsibility for decisions about risks associated with AI system development and deployment.” ¹

What the operator must do:
You must (1) name the executive leadership role(s) accountable for AI risk decisions, (2) define the scope of decisions they own (risk acceptance, exceptions, deployment approvals, and risk appetite), and (3) maintain a repeatable governance process that produces a defensible audit trail tying each material AI risk decision to a specific AI system and release. ¹

Plain-English interpretation

This requirement means executives cannot treat AI risk as “a technical detail handled by the model team.” If an AI system creates legal, customer, safety, financial, or reputational risk, executive leadership must be responsible for the decision to proceed, pause, remediate, or retire. “Responsibility” is proven through decision records, not verbal awareness. ¹

In practice, most organizations satisfy GOVERN-2.3 by:

• Establishing an AI governance forum with executive decision authority (or a named executive approver for AI risk).
• Defining decision gates that block deployment until risk is reviewed and approved.
• Requiring documented risk acceptance (including exceptions) that is traceable to each system. ²

Who it applies to

Entity scope: Any organization developing or deploying AI systems, including service organizations providing AI-enabled services to customers. ¹

Operational contexts where auditors will test this hard:

• Customer-facing decisioning (eligibility, pricing, ranking, moderation, fraud).
• Internal AI that impacts regulated processes (compliance monitoring, HR, finance controls).
• AI supplied by a third party but configured or deployed by you (you still own deployment risk decisions).
• Material model changes: new training data, architecture changes, new prompts/guardrails, or expanded use cases. ²

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

Step 1: Assign executive accountability (name the role, not a committee)

Create a single “Accountable Executive for AI Risk” role, even if decisions are made through a committee. Common choices: COO, CRO, CISO, CIO/CTO, or a business-line executive for the highest-risk AI portfolio. Document:

• Decision authority (approve/deny/accept risk)
• Escalation triggers
• Delegation rules (what can be delegated, to whom, and when it must return to the executive) ¹

Practical tip: If you cannot get one executive owner, designate a primary and a backup, and define how ties are broken. Ambiguity is a control failure during an exam.

Step 2: Define “material AI risk decisions” and when leadership must act

Write a short list of decision types that require executive action. Keep it tight and enforceable:

• Approval to deploy a new AI system to production
• Approval to expand to a new population, geography, or use case
• Risk acceptance for known high-severity issues (documented exceptions)
• Approval to continue operating after a high-severity incident until remediation is complete
• Retirement decisions when risks cannot be reduced to acceptable levels ²

Then define trigger events that force review (examples: “new model,” “major model change,” “new training dataset,” “new third-party model provider,” “sustained performance drift,” “validated harm report”). ²

Step 3: Establish decision gates in your delivery lifecycle

Embed governance into operational workflows so it runs consistently:

• Intake gate: system registration, use-case definition, owner assignment
• Pre-production gate: risk assessment reviewed, mitigations planned, residual risk documented
• Launch gate: executive sign-off (or documented decision) required to deploy
• Post-launch gate: monitoring review, incidents and drift reviewed, continuation decision when needed ¹

If you already have change management, align the AI gate to change tickets: no approval, no release.

Step 4: Create a decision matrix (RACI + thresholds)

Build a one-page matrix that maps risk level to approver:

• Low risk: product owner + compliance sign-off
• Medium risk: AI governance forum approval
• High risk: accountable executive approval required (and legal/privacy consulted) ²

Avoid pretending the matrix is “objective” if you do not have measurable criteria. Keep criteria operational: user impact, automation level, reversibility, regulatory sensitivity, and third-party dependency.

Step 5: Standardize the “AI Risk Decision Memo” (your minimum viable artifact)

Each material decision should produce a memo (or structured record) that includes:

• System name, owner, and intended use
• Summary of key risks (privacy, security, bias/fairness, safety, explainability, misuse)
• Controls and mitigations in place
• Residual risk statement (what remains true even after mitigations)
• Decision requested (approve/deny/approve with conditions/accept exception)
• Executive decision, date, and conditions ²

Keep it readable. Executives will not sign what they cannot understand.

Step 6: Implement exception handling that proves responsibility

You need an exception workflow for cases where teams want to ship with known gaps. Require:

• Specific exception scope (what control is waived, for what system, for how long)
• Compensating controls
• Time-bound remediation plan and owner
• Executive approval and periodic re-attestation until closure ²

Step 7: Run control health checks (prove it operates, not that it exists)

On a recurring cadence, test:

• Did all in-scope AI deployments have an executive decision record?
• Were conditions met (e.g., “monitoring enabled,” “human-in-the-loop active”)?
• Were exceptions closed on time or re-approved? ²

Tools like Daydream help by turning this requirement into a control card with owners, triggers, and evidence expectations, then tracking recurring health checks and remediation to validated closure.

Required evidence and artifacts to retain

Minimum evidence bundle (retain in a controlled repository, linked to each AI system):

1. AI governance charter (executive accountability, decision rights, escalation) ¹
2. Decision matrix / RACI for AI risk approvals ²
3. AI system inventory entries (what is deployed, who owns it, where it runs) ²
4. AI Risk Decision Memos for launches and material changes ²
5. Meeting agendas/minutes showing decisions made and attendees ²
6. Exception register with approvals, expirations, and closure evidence ²
7. Control health check results and remediation tracking ²

Common exam/audit questions and hangups

Auditors, customer diligence teams, and internal assurance typically press on:

• “Who is the executive accountable for AI risk decisions? Show me the document.” ¹
• “Show me the last AI deployment. Where is the executive decision record?” ²
• “What triggers an escalation? Prove you followed it in an incident or exception.” ²
• “How do you handle third-party AI models? Who accepted the deployment risk?” ²
• “How do you know this process runs every time?” (They want sampling evidence and health checks.) ²

Hangup to expect: teams produce a slide deck about “Responsible AI principles” but cannot map it to specific go/no-go decisions.

Frequent implementation mistakes (and how to avoid them)

1. Mistake: committee ownership with no accountable executive.
   Fix: name an accountable executive even if a committee recommends. Record the final decision-maker. ¹

2. Mistake: approvals happen after launch.
   Fix: tie approval to release management so deployment is blocked without the decision artifact. ²

3. Mistake: “risk accepted” without stating the residual risk.
   Fix: require a short residual risk statement and conditions for operation (monitoring, rollback plan, human review). ²

4. Mistake: exceptions become permanent.
   Fix: track expirations and require re-approval or closure evidence. Add health checks. ²

5. Mistake: third-party AI treated as “outsourced risk.”
   Fix: keep third-party due diligence and contract review, but still require your executive decision to deploy and accept residual risk. ²

Enforcement context and risk implications

No specific public enforcement cases were provided in the source catalog for this requirement. ²

Operational risk remains: if an AI system causes customer harm, privacy impact, discrimination allegations, security failures, or material business disruption, you will be asked who approved the risk and what they reviewed. GOVERN-2.3 is how you answer with evidence instead of narrative. ¹

Practical execution plan (30/60/90-day)

The goal is speed with governance quality. Use phases rather than time-bound promises.

Phase 1 (Immediate): establish accountability and decision gates

• Assign the accountable executive and publish decision rights. ¹
• Stand up the AI risk decision memo template and an exception register. ²
• Identify your in-scope AI systems and prioritize the ones already in production. ²

Phase 2 (Near-term): wire into operations

• Integrate the approval gate into change management/release workflows. ²
• Train product, engineering, and data science leads on triggers and escalation. ²
• Run a retroactive review for high-impact systems already deployed; capture executive risk acceptance or remediation decisions. ²

Phase 3 (Ongoing): prove sustained operation

• Run periodic control health checks and track remediation to closure. ²
• Report to executive leadership: open exceptions, incident trends, and upcoming approvals needed. ²
• Use tooling (for example, Daydream) to standardize control cards, evidence bundles, and recurring attestations across teams.

Frequently Asked Questions

Does GOVERN-2.3 require the CEO to sign every model release?

No. It requires executive leadership to take responsibility for AI risk decisions, which you can operationalize via a named accountable executive and a decision matrix that defines when escalation is required. ¹

We use a third-party foundation model. Do we still need an executive risk decision?

Yes for deployment risk. Third-party due diligence helps, but your organization still decides whether your use case, configuration, and monitoring are acceptable for production. ²

What counts as “taking responsibility” in evidence terms?

A dated, attributable decision record tied to a specific AI system and release, supported by the risk memo inputs leadership reviewed (or a reference to them). Meeting minutes and approval workflow records are common proof. ²

Can leadership delegate these decisions to a model risk committee?

Leadership can delegate execution, but you still need clear accountability and documented decision rights. If a committee approves, show the executive delegation and the committee’s authority and membership. ¹

How do we handle fast iterations like prompt changes?

Define what is “material” and route only material changes through executive approval. For non-material changes, require documented review by the delegated approver and keep the change log tied to the system record. ²

What’s the minimum viable process for a smaller organization?

Name one accountable executive, require a short risk decision memo for each production AI system, and block deployment until that decision is recorded. Add an exception register so deviations are explicit and time-bound. ²

Footnotes

1. NIST AI RMF Core

2. NIST AI RMF 1.0