MEASURE-2.4: The functionality and behavior of the AI system and its components – as identified in the map function – are monitored when in production.

MEASURE-2.4 requires you to continuously monitor the in-production AI system’s functionality and behavior, including each component identified during your MAP function, and to act on monitoring results through defined thresholds, triage, and change control. Operationalize it by instrumenting model and pipeline telemetry, setting alerts tied to risk limits, and retaining auditable evidence of reviews and remediations. ¹

Key takeaways:

• Monitor the whole AI “stack” in production: model, data, features, prompts, integrations, and human workflows identified in MAP. ¹
• Define measurable thresholds (risk limits) and an escalation path, then connect alerts to incident and change management.
• Keep evidence: dashboards, alerts, weekly/monthly review notes, incident tickets, and post-change validation.

MEASURE-2.4 is an operations requirement disguised as a risk principle: once an AI system ships, you must keep watching it. The MAP function defines what “it” is: the system boundary, components, dependencies, upstream data sources, downstream consumers, human-in-the-loop steps, and third parties. MEASURE-2.4 then expects monitoring of the functionality and behavior of that mapped system while it runs in production, not only during testing. ¹

For a Compliance Officer, CCO, or GRC lead, the fast path is to treat MEASURE-2.4 like a control that looks a lot like production controls for security and reliability, but with AI-specific signals: drift, data quality, performance by cohort, unsafe outputs, prompt abuse, tool-calling failures, and policy violations. The goal is not perfect detection; it is a defensible, repeatable operating cadence where issues are discoverable, owners respond, changes are governed, and evidence is retained. ¹

This page gives requirement-level implementation guidance you can hand to engineering, model risk, and operations teams, then audit against.

Regulatory text

Text (excerpt): “The functionality and behavior of the AI system and its components – as identified in the map function – are monitored when in production.” ¹

What the operator must do:

1. Use your MAP outputs to list the AI system components that matter (model(s), data pipelines, feature stores, prompts, retrieval, guardrails, tools, human review steps, third parties). ¹
2. Implement monitoring in production for both functionality (does it work as designed?) and behavior (does it behave within risk tolerances?) across those components. ¹
3. Run monitoring as an operational control: defined metrics, thresholds, alerting, response, and documented review. ¹

Plain-English interpretation

You are responsible for catching AI issues after launch, not learning about them from customers, regulators, or the press. “Monitoring” here means measurable signals with ownership and follow-through, not a one-time dashboard. “Functionality” covers basic correctness and availability (inputs accepted, outputs produced, latency, integrations). “Behavior” covers whether the model’s outputs and decisions stay aligned with intended use, policy, and risk limits (quality, bias/fairness signals where applicable, safety, privacy leakage, harmful content, automation errors).

A useful test: if your system started failing silently (data feed changed, prompt template updated, third-party model version changed), would you detect it quickly and prove you responded appropriately? MEASURE-2.4 expects “yes,” and expects the proof. ¹

Who it applies to

Entity types: organizations developing or deploying AI systems. ¹

Operational contexts where auditors will expect this control:

• Customer-facing AI features (chat, recommendations, pricing, underwriting support, fraud flags, content moderation).
• Internal decision-support AI that influences regulated or high-impact processes (HR screening, eligibility, claims handling).
• Any AI system with third-party dependencies (hosted models, APIs, data brokers, annotation services), since component behavior can change without your code changing.

Control owners (typical):

• Primary: AI product owner + ML engineering/ML Ops lead.
• Shared: Security (telemetry/IR), Risk/Model Risk (thresholds), Compliance/GRC (control design and evidence), Data engineering (data quality monitoring), Third-party risk (provider monitoring and SLAs).

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

1) Convert MAP outputs into a “monitoring inventory”

Create a table that lists each mapped component and its monitoring coverage. Minimum columns:

• Component name and type (model, data source, feature pipeline, prompt template, retrieval index, guardrail, UI, human review queue, third-party service). ¹
• Failure modes (what can go wrong in production).
• Monitoring signals (metrics/logs) and where they are captured.
• Thresholds and alert severity.
• Owner and on-call/escalation path.
• Evidence location (dashboard link, log index, ticket queue).

2) Define production monitoring signals that match “functionality” and “behavior”

Build a baseline set, then add AI-specific signals per use case.

Functionality monitoring (examples you can standardize):

• Input pipeline health: schema changes, null spikes, missing fields, out-of-range values.
• System reliability: error rates, timeouts, latency, throughput.
• Integration checks: retrieval availability, tool-calling success rate, API dependency failures.
• Output delivery: downstream service acceptance, queue backlogs, human-review SLA breaches.

Behavior monitoring (examples you can tailor):

• Model performance signals: accuracy/proxy labels where available, calibration, acceptance/override rates for decision-support, user dissatisfaction markers.
• Drift and shift: feature distribution changes, embedding distribution changes, prompt distribution changes for LLM apps.
• Safety/policy: disallowed content, unsafe instructions, PII in outputs, policy-violating tool calls, jailbreak indicators.
• Cohort monitoring where relevant: performance and error patterns by region, channel, device type, or other approved segments tied to risk hypotheses from MAP.

Write down which signals are “must alert” versus “investigate on cadence.” Auditors will accept maturity stages if you can show prioritization tied to risk.

3) Set thresholds (risk limits) and link them to response playbooks

For each “must alert” signal, document:

• Trigger definition (metric + condition).
• Severity mapping (e.g., high = immediate triage; medium = next business day).
• Required actions (disable feature flag, fall back to rules, route to human review, roll back model, block tool execution, notify third party).
• Who decides and who executes.
• How you document closure.

Keep playbooks short. One page per major failure mode is enough if it is actionable.

4) Instrument and centralize telemetry

Engineering should implement:

• Logging for inputs, outputs, and key decision metadata (with appropriate privacy controls).
• Model/pipeline versioning in logs (so you can correlate incidents to changes).
• Dashboards for the signals in your inventory.
• Alert routing to an incident system (pager/email/chat plus ticketing).

From a GRC standpoint, your job is to ensure monitoring is not trapped in a developer’s laptop. Evidence needs stable retention.

5) Establish an operating cadence

Create a recurring review rhythm:

• Operational review: owners review dashboards and alerts, document findings, and open tickets.
• Risk review: periodic review of thresholds and coverage against MAP changes (new data sources, new prompts, new third parties). ¹

Tie this to change management: any material AI change should include “post-deploy monitoring verification” and “back-out criteria.”

6) Govern third-party and component change risk

MEASURE-2.4 explicitly includes “components,” which often includes third parties. Build triggers for:

• Third-party model/version updates.
• API behavior changes.
• Data provider feed changes.
• New regions or user segments.

Minimum expectation: you can detect a change (or its effects), you can pause/roll back, and you can document the decision path.

7) Prove it works with a lightweight test

Run a “monitoring control test”:

• Simulate one data quality failure and one unsafe-output case in a non-production or controlled production setting.
• Confirm alerts fire, tickets are created, and the owner follows the playbook.
• Store the test record as evidence of control operation.

Required evidence and artifacts to retain

Keep evidence in a way an auditor can sample without bespoke exports.

Core artifacts (retain continuously):

• Monitoring inventory mapped to MAP components (current version + prior versions). ¹
• Dashboards/screenshots or exported reports showing defined metrics over time.
• Alert definitions and routing rules (configuration exports).
• Incident/ticket records: detection time, triage notes, actions taken, closure approval.
• Change records: model/prompt/data changes with post-deploy verification notes.
• Meeting notes or attestations for monitoring reviews (who reviewed, what was found, what changed).

Nice-to-have artifacts (helps in hard exams):

• Post-incident reviews for significant events.
• Evidence that monitoring coverage is updated when MAP changes (diff logs, approvals). ¹

If you use Daydream, set MEASURE-2.4 up as a recurring control with an assigned owner, required monthly evidence prompts, and automated collection links to dashboards and ticket queues. That reduces the “we do it but can’t prove it” gap that sinks audits.

Common exam/audit questions and hangups

• “Show me your MAP output and where each mapped component is monitored in production.” ¹
• “Which metrics are tied to risk limits, and who approved the thresholds?”
• “How do you detect drift or behavior change after a model update or data change?”
• “What happens when the system produces a harmful or policy-violating output?”
• “How do you monitor third-party model/API behavior changes?”
• “Prove monitoring is ongoing: give me evidence samples from different months.”

Hangup: teams show dashboards but cannot show review, decisions, and closure. Monitoring without an operating cadence reads as aspirational.

Frequent implementation mistakes (and how to avoid them)

1. Monitoring only the model, not the system.
   Fix: include data pipelines, prompts, retrieval, guardrails, and human queues from MAP in the monitoring inventory. ¹

2. No thresholds, only charts.
   Fix: define “what good looks like” and “what requires action,” even if initial thresholds are conservative and refined over time.

3. No link to incident/change management.
   Fix: alerts must create tickets; tickets must reference the model/pipeline version and the remediation path.

4. Ignoring third-party change vectors.
   Fix: add provider release monitoring, version pinning where possible, and “provider-change” incident categories.

5. Evidence scattered across tools with no retention plan.
   Fix: define evidence-of-operation requirements (what, where, who) and enforce them through a recurring GRC control check.

Enforcement context and risk implications

NIST AI RMF is a framework, not a standalone regulator-imposed rule in this excerpt, so “enforcement” typically shows up indirectly: contractual commitments, internal governance, or as a benchmark used by auditors, customers, and regulators evaluating whether your AI risk program is reasonable. If you cannot show production monitoring, the risk is less about a technical miss and more about governance failure: you will struggle to rebut claims that harms were foreseeable and preventable.

Operationally, weak MEASURE-2.4 controls drive:

• Undetected performance regressions after releases.
• Safety and content incidents that could have been caught with output monitoring and triage.
• Inability to explain adverse outcomes because logs and versioning are incomplete.

A practical 30/60/90-day execution plan

First 30 days (stand up the control)

• Confirm the AI system boundary and component list from MAP; publish the monitoring inventory draft. ¹
• Pick initial must-have signals: reliability, data quality, version tracking, basic output policy checks.
• Assign owners and define escalation paths for each alert category.
• Decide where evidence lives (dashboards + ticketing + GRC repository).

Days 31–60 (operationalize and prove)

• Implement alerting tied to thresholds for the highest-risk failure modes.
• Connect alerts to incident tickets; require closure notes and remediation classification.
• Create two short playbooks: data feed anomaly and unsafe output.
• Run a control test (tabletop or controlled simulation) and store results.

Days 61–90 (mature and harden)

• Expand behavior monitoring: drift, cohort signals where relevant, prompt/retrieval health for LLM apps.
• Add third-party monitoring triggers and provider-change review.
• Implement a recurring monitoring review meeting with documented minutes.
• Add post-deploy monitoring verification to change management for model/prompt/data updates.

Frequently Asked Questions

Do we need monitoring if the model is “static” and rarely updated?

Yes. Inputs, user behavior, third-party dependencies, and operating environments change even when weights do not. MEASURE-2.4 is about production behavior over time, not only release management. ¹

What counts as “components” for an LLM application?

Treat prompts, retrieval indexes, guardrails, tool-calling logic, routing, and human review steps as components, alongside the underlying model. If MAP identified it as part of the system, MEASURE-2.4 expects production monitoring for it. ¹

We don’t have ground truth labels in production. How can we monitor behavior?

Use proxy signals such as complaint rates, override/appeal rates, human reviewer disagreement, policy-violation detections, and drift metrics. Document why each proxy is reasonable for the risk you are managing.

How do we set thresholds without historical baselines?

Start with conservative thresholds tied to obvious failures (schema breaks, error spikes, disallowed content detection), then tighten as you collect baseline data. Record the approval and the rationale so auditors see a controlled process.

What evidence is most persuasive to auditors?

Time-stamped alert history tied to tickets that show triage, decision, remediation, and closure, plus the monitoring inventory mapped to MAP components. Dashboards alone rarely satisfy “operating effectiveness.” ¹

How does third-party risk management connect to MEASURE-2.4?

If a third party provides a model, data, or API, it is a monitored component in production. Require provider change notices where possible, monitor provider-driven behavior changes, and document your response when upstream changes affect outputs. ¹

Footnotes

1. NIST AI RMF Core