GOVERN-1.5: Ongoing monitoring and periodic review of the risk management process and its outcomes are planned and organizational roles and responsibilities clearly defined, including determining the frequency of periodic review.

To meet GOVERN-1.5, you must run a defined, repeatable cadence of ongoing monitoring and periodic reviews for your AI risk management process and its outcomes, with named owners, clear responsibilities, and documented review frequency. Operationalize it by assigning roles, setting triggers and schedules, running review meetings, and retaining an evidence bundle each cycle. ¹

Key takeaways:

• Define “who does what, when” for AI risk monitoring and periodic RMF reviews, and document it. ¹
• Review both the process (how you manage risk) and outcomes (what risks and incidents occurred, what changed). ¹
• Keep an auditable evidence trail: inputs, decisions, actions, and verified closure of remediation items. ²

GOVERN-1.5 is a governance control, not a modeling control. It asks a simple question regulators, customers, and internal audit will all ask: “How do you know your AI risk management process still works, and who is accountable for proving it?” NIST’s AI Risk Management Framework (AI RMF) expects planned ongoing monitoring and periodic review of both the risk management process and the outcomes it produces, with clearly defined organizational roles and responsibilities, including the frequency of those reviews. ¹

For a CCO or GRC lead, the fastest path is to treat this like any other ongoing governance control: write a control card, set a monitoring and review cadence, assign accountable owners, define escalation paths, and standardize the evidence bundle. Then run it repeatedly. The work is less about drafting a policy and more about building a lightweight operating rhythm across Legal/Compliance, product, engineering, security, and the business owner of the AI system.

This page gives requirement-level implementation guidance you can put into a control library and run as an operational process, including a practical execution plan and an audit-ready evidence checklist aligned to NIST AI RMF. ²

Regulatory text

Excerpt (GOVERN-1.5): “Ongoing monitoring and periodic review of the risk management process and its outcomes are planned and organizational roles and responsibilities clearly defined, including determining the frequency of periodic review.” ¹

Operator interpretation (what you must do):

1. Plan ongoing monitoring: define what signals you watch (performance drift, incidents, complaints, policy exceptions, third-party changes) and how those signals get reviewed and escalated. ¹
2. Plan periodic review: set a documented review cadence for the AI risk management process and the outcomes, not ad hoc “when something goes wrong.” ¹
3. Define roles and responsibilities: assign accountable owners (risk process owner, system owner, control operators, approvers) and document who can accept risk, who must remediate, and who validates closure. ¹
4. Determine frequency: explicitly decide how often periodic reviews happen and what events force an out-of-cycle review. ¹

Plain-English interpretation of the requirement

You need a scheduled, owned, and provable way to check whether:

• Your AI risk management process is being followed (intake, assessment, approvals, monitoring, incident handling).
• The process outputs are acceptable (known risks, residual risk decisions, incidents/near misses, user impacts, third-party performance, and remediation effectiveness). ¹

If your “process” is a set of documents but you cannot show a consistent operating cadence, named owners, and evidence that monitoring and review happened, you will fail this requirement in practice.

Who it applies to (entity and operational context)

GOVERN-1.5 applies to:

• AI developers building models/systems.
• Organizations deploying AI systems (even if a third party built the model).
• Service organizations providing AI-enabled services to customers. ¹

Operationally, it applies wherever AI risk exists:

• AI embedded in customer-facing decisions (eligibility, pricing, claims, hiring, content moderation).
• AI supporting internal operations (fraud detection, productivity copilots, forecasting).
• AI supplied by third parties (SaaS AI features, foundation model APIs, data labeling providers). ²

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

Step 1: Create a GOVERN-1.5 control card (your runbook)

Build a one-page control definition that an auditor and a control operator can both execute. Include:

• Objective: ensure ongoing monitoring + periodic review of AI RM process and outcomes. ¹
• Scope: which AI systems, business units, and third parties are covered.
• Control owner: typically the AI governance lead, ERM owner, or CCO-delegated GRC owner.
• Control operators: system owners, model risk, security, privacy, product.
• Approvers: risk committee, AI governance committee, or named executive(s).
• Cadence: defined periodic review frequency and ongoing monitoring rhythm. ¹
• Trigger events: what forces an out-of-cycle review (material model change, new use case, incident, major data source change, third-party change).
• Outputs: meeting minutes/decisions, updated risk register entries, remediation tickets, revised thresholds/metrics.
• Exception process: how missed reviews are handled and documented.

Recommended pattern: treat this as a recurring governance control with a calendar schedule and ticketed outputs, not a “policy says we review” statement. ²

Step 2: Define roles and responsibilities (RACI that maps to decisions)

Create a RACI that answers four exam-grade questions:

• Who monitors defined signals and metrics?
• Who runs periodic reviews and sets agendas?
• Who approves risk acceptance and residual risk decisions?
• Who verifies remediation closure and effectiveness? ¹

Minimum roles to name:

• AI System Owner (business accountability)
• Risk Management Process Owner (governance accountability)
• Compliance/Legal (policy alignment, regulatory obligations)
• Security (monitoring for abuse, access, incident response)
• Privacy (data use, purpose limits, notices)
• Engineering/ML (model changes, monitoring instrumentation)
• Third-party owner (if models/data are sourced externally)

Step 3: Set monitoring inputs and thresholds (what you watch)

Ongoing monitoring should have defined inputs that are actually obtainable. Typical categories:

• Model/system performance: drift indicators, error rates, coverage gaps, fallback frequency.
• Harm and complaint signals: customer complaints, support tickets, adverse impact reports.
• Security signals: abuse patterns, prompt injection attempts, data exfiltration indicators.
• Change management: deployments, retraining, new features, new datasets.
• Third-party signals: SLA issues, subprocessor changes, material contract changes. ²

Document which inputs are required for each in-scope system and where they come from (SIEM, MLOps, ticketing, GRC tool, vendor management system).

Step 4: Define the periodic review cadence (and justify it)

GOVERN-1.5 requires you to determine and document review frequency. ¹

Use a risk-tiered cadence:

• Higher-impact AI systems: more frequent review and tighter trigger events.
• Lower-impact systems: periodic review still occurs, but with lighter weight evidence.

Your documentation should state:

• Periodic review frequency by system tier
• What happens during the review
• Required attendees/approvers
• What artifacts must be updated (risk register, control status, open issues)

Step 5: Run the review meeting and produce decisioned outputs

Each cycle should produce:

• Agenda mapped to monitoring inputs and open risk items
• Decisions: accept risk, require remediation, pause a feature, add monitoring, update thresholds
• Actions with owners and due dates
• Escalations for overdue or high-risk items ¹

Keep the meeting disciplined: if no decisions are made and nothing is updated, you will struggle to show this control “operates.”

Step 6: Track remediation to validated closure (control health)

Maintain a single backlog for AI risk remediation items:

• Ticket ID, owner, due date, risk rating, affected system
• Evidence of fix
• Validation step (QA/security/compliance sign-off)
• Closure date and post-fix monitoring plan ²

This is where many programs fail: they can show “issues were identified” but not “issues were closed and verified.”

Required evidence and artifacts to retain

Build a standard “minimum evidence bundle” per cycle. Keep it consistent.

Evidence item	What it proves	Owner
GOVERN-1.5 control card	Planned monitoring/review + defined cadence, triggers, roles	GRC/Compliance
RACI / role definitions	Clear responsibilities and decision authority	GRC + HR/Leadership
Monitoring dashboard exports / reports	Ongoing monitoring occurred and was reviewed	System owner / MLOps / Security
Periodic review agenda + attendance	The review was planned and executed	Process owner
Minutes / decision log	Outcomes were evaluated and decisions made	Committee secretary / GRC
Updated risk register entries	Outcomes feed back into risk management	Risk owner
Remediation tickets + closure evidence	Issues tracked to validated closure	Control operators
Exception records	Missed cadence handled with governance	GRC/Compliance

Retention location should be explicit (GRC tool, document repository, ticketing system) and access-controlled.

Daydream can help by standardizing the control card, enforcing evidence checklists per cycle, and keeping decision logs tied to systems and third parties so you can answer diligence questionnaires without rebuilding the story each time. ²

Common exam/audit questions and hangups

Expect these questions from internal audit, customers, and regulators assessing your AI governance:

1. “Show me your defined periodic review frequency and who approved it.” They want a documented cadence, not informal practice. ¹
2. “Who is accountable for ongoing monitoring for this specific AI system?” Naming a team is weaker than naming a role/person and their responsibilities. ¹
3. “Show the last review, its inputs, decisions, and what changed afterward.” Minutes without actions, or actions without closure evidence, triggers findings. ²
4. “How do third-party AI components factor into monitoring and review?” You need signals and contract hooks, not only initial due diligence. ²

Frequent implementation mistakes and how to avoid them

• Mistake: policy-only compliance. A policy saying “we monitor” without a schedule, agenda, and evidence bundle fails under scrutiny.
  Fix: publish a control card and recurring calendar events, with templated outputs. ²

• Mistake: monitoring exists but is not governed. Teams have dashboards, but no one reviews them on a defined cadence or logs decisions.
  Fix: require decision logs tied to monitoring outputs for in-scope systems. ¹

• Mistake: outcomes not reviewed. Reviews focus on whether controls were “done,” not whether harms/incidents occurred or residual risk increased.
  Fix: split the agenda into “process health” and “outcome health” every cycle. ¹

• Mistake: no out-of-cycle triggers. Material changes slip through until the next scheduled review.
  Fix: tie trigger events to change management and incident response workflows. ²

Enforcement context and risk implications

No public enforcement cases were provided in the source catalog for this requirement. Practically, the risk is exam and customer diligence failure: you cannot demonstrate sustained governance, which raises the likelihood of untracked model changes, unowned remediation items, and inconsistent risk acceptance decisions. ²

Practical 30/60/90-day execution plan

Days 1–30 (Stand up the operating model)

• Inventory in-scope AI systems and identify system owners. ²
• Draft the GOVERN-1.5 control card: cadence, triggers, roles, outputs. ¹
• Publish the RACI and define risk acceptance authority for AI-related risks. ¹
• Define the minimum evidence bundle and where it will be stored. ²

Days 31–60 (Start running the control)

• Implement monitoring inputs for each system (or document gaps and a plan). ²
• Hold the first periodic review for the highest-risk systems; produce decision logs and tickets. ¹
• Connect trigger events to change management so out-of-cycle reviews are initiated automatically when defined changes occur. ²

Days 61–90 (Prove sustained operation)

• Run a second review cycle (or expand to additional systems) using the same templates. ¹
• Perform a control health check: confirm evidence completeness, ticket closure quality, and escalation effectiveness. ²
• Tune cadence by tier, based on early findings, and record the rationale and approval. ¹

Frequently Asked Questions

How do I decide the “frequency of periodic review” without overcommitting the business?

Tier your AI systems by impact and set different review cadences by tier, with out-of-cycle triggers for material changes. Document the rationale and approval so the frequency is defensible in audit. ¹

What counts as “ongoing monitoring” for GOVERN-1.5?

Ongoing monitoring is a defined set of signals you regularly review, tied to the AI system’s risks and expected harms, plus an escalation path when thresholds are breached. Keep evidence that monitoring outputs were reviewed and acted on. ¹

Do I need a committee, or can a single owner run the periodic review?

A committee is optional, but roles and responsibilities must be clear and decision authority must be defined. For higher-risk systems, a cross-functional review group is often the cleanest way to show governance. ¹

How do we handle third-party AI models or SaaS AI features under this requirement?

Treat the third party as part of your monitoring inputs: track service changes, incidents, SLA issues, and contract changes, and include them in periodic reviews. Your system owner still needs accountability for outcomes in your environment. ²

What’s the minimum evidence I should keep if storage and bandwidth are tight?

Keep the control card, RACI, last review agenda/minutes with decisions, monitoring snapshots or reports, and remediation tickets with closure validation. Standardize the bundle so each cycle produces the same evidence set. ²

How do I prove we reviewed “outcomes,” not just the process?

Include outcome metrics and incident/complaint summaries as required agenda items, and show decisions tied to those outcomes (threshold changes, additional controls, remediation). Update the risk register to reflect what you learned. ¹

Footnotes

1. NIST AI RMF Core

2. NIST AI RMF 1.0