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Build monitoring around the harms your AI system could cause and the performance it must sustain: define its intended use, set measurable baselines and risk tolerances, test under realistic conditions, watch production behavior, and assign people who can investigate and intervene. There is no universal metric or monitoring interval that fits every system. NIST’s AI Risk Management Framework (AI RMF) and Generative AI Profile provide voluntary guidance for tailoring the measures and response to context.
Start with the system’s context and the harms that matter
Before choosing dashboards or alerts, document what the system is intended to do and how it is actually deployed. Include its components and dependencies, the conditions in which people use it, and the individuals or groups who may be affected. A model used to draft low-stakes internal text presents a different risk profile from one whose outputs influence consequential decisions.
List plausible harms and failure modes for that specific context. For a generative system, relevant risks may include harmful bias, privacy violations, offensive or violent content, misleading answers, or assistance with inappropriate, malicious, or illegal requests. Include known problems, prior incidents and near misses, external feedback, and input from people with domain expertise. Not every trustworthiness characteristic applies in the same way to every system.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsFor each material risk, record the potential impact, who could be affected, and what level of residual risk the organization is prepared to tolerate. Make important risks visible even when available methods cannot measure them reliably; an unmeasurable risk is not evidence that the risk is absent.
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Choose measures that cover both safety and performance
Use a set of measures rather than relying on a single headline accuracy score. Each measure should connect to a defined risk or operational requirement, explain how it is calculated, and identify its limitations. NIST’s Generative AI Profile states: “Safety metrics reflect system reliability and robustness, real-time monitoring, and response times for AI system failures.” The right selection of metrics remains specific to the system and its use.
| Monitoring area | Questions the measure should answer | Example evidence to consider |
|---|---|---|
| Output safety | How often does the system produce outputs in a harm category that matters here, and how well does it handle risky requests? | Rates of relevant harmful outputs in an evaluated sample; results for inappropriate or malicious requests and attempts to bypass safeguards. |
| Task quality and errors | Does the system still perform its intended task, and what errors matter most to users or affected people? | Task-specific quality and error measures, evaluated with a documented method and representative cases. |
| Reliability and robustness | Does behavior remain dependable across relevant conditions, and does the system fail safely? | Results across deployment-like conditions, including known failure modes and stress scenarios. |
| Operational health | Can people use the system reliably, and how quickly can failures be handled? | Response times, downtime, out-of-range performance, incident response, and other system-function measures relevant to the deployment. |
For output-rate measures, define what counts as an event, the denominator, the sampling or evaluation method, and who labels borderline cases. Keep those definitions stable enough to compare results over time; if a definition or evaluator changes, record the change because it may alter the apparent rate. For each measure, specify the population, time window, and conditions it represents. Document risks for which no adequate measurement method is available.
Establish a baseline before release
A baseline gives later results a reference point. Before deployment, document the test sets, metrics, tools, system version, test conditions, and observed results, including uncertainty and known limitations. Use simulations and test conditions that resemble expected use as closely as practical; a result from a narrow test set should not be presented as a guarantee about all real-world behavior.
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Set performance benchmarks and risk tolerances before interpreting production changes. For each measure, decide what result is acceptable, what change warrants investigation, and who owns that decision. The threshold should reflect the consequences of failure and the reliability of the measurement—not merely what is convenient to alert on. NIST recommends benchmarking and documentation but does not prescribe one set of metrics for every AI system.
Stress-test likely changes and known failure modes
Evaluate scenarios that could make the deployed system behave differently from the baseline. These may include concept drift, changing user inputs, high load, and conditions associated with past incidents or near misses. Consult domain experts when deciding which cases are plausible and consequential.
- Record the conditions tested, the system version, and the range and limits of the test.
- Check whether safeguards continue to work on inappropriate or malicious requests and attempts to bypass them, where those risks apply.
- Observe whether performance degrades under stress and whether the system fails safely.
- Keep stress-test results distinct from routine production results so that their different conditions are clear.
A stress test can reveal weaknesses; passing one does not establish that the system is safe in every condition.
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Monitor production behavior, not just the model score
Once deployed, monitor the system’s relevant functionality and behavior in its actual operating context. Track the measures tied to the identified harms alongside quality, error, and operational measures. Watch for sustained changes and out-of-range performance, as well as incidents, response times, and downtime where relevant.
Production evidence may include direct evaluations, incident and error reports, user or operator feedback, and system-health signals. Choose collection methods that make sense for the system and handle data consistently with applicable privacy and data-handling requirements. Monitoring should not assume that every output can or should be retained; document what is collected, how it is used, and the limits this creates for detection.
Distinguish a change in measured performance from a change in the measurement process. A new evaluator, revised category definition, different sampling method, or shift in the inputs being evaluated can change the reported result without demonstrating that the model itself changed. Preserve enough information about versions, conditions, and evaluation methods to interpret alerts.
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Set a risk-based evaluation cadence
NIST calls for regular evaluation and monitoring but does not set one universal interval. Choose a cadence based on the severity and likelihood of harm, how quickly the system or its operating environment can change, how much real-time visibility is needed, and the cost of missing a failure. A consequential system or one exposed to rapidly changing conditions may need more frequent checks and faster alert review than a stable, low-impact use.
Plan evaluation at more than one point in the lifecycle: before release, during routine operation, and when a meaningful change or incident could alter the risk picture. Define what counts as a trigger for an additional review—for example, a system modification, a notable shift in inputs, or a relevant incident—and assign an owner to decide whether the existing measures remain adequate.
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Define alert thresholds, owners, and interventions
An alert is useful only if someone knows what it means and what authority they have. Link thresholds to the documented risk tolerances, name the team or role responsible for review, and define the actions available at different levels of severity. Treat alert values as a basis for investigation rather than proof of a cause, particularly when the underlying sample is small or the measure is uncertain.
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- Detect: Route a threshold breach, incident report, or other relevant signal to the designated owner, with the measure, time window, system version, and supporting context.
- Assess: Check the evidence, scope, potential impact, and whether the signal reflects a genuine system change or a measurement change. Escalate when the possible harm warrants it.
- Contain and respond: Depending on severity and context, options may include investigating, mitigating the issue, adding human review, recalibrating or modifying the system, or shutting it down.
- Record and learn: Document the incident, decisions, timing, actions, and outcome. Use the result to improve tests, controls, and response procedures.
Practice the response path before it is needed. NIST’s Playbook suggests collecting safety statistics such as out-of-range performance, incident response times, downtime, and injuries; it does not provide a topic-specific empirical rate. Teams can use relevant operational measures to assess whether they detect, respond to, and recover from problems effectively.
Review the monitoring plan as risks change
Monitoring measures can become stale as the system, its users, the environment, or understanding of its impacts changes. Regularly check whether the metrics still represent the important risks, whether controls work as intended, and whether incidents or feedback reveal harms that the current test set misses. Compare observed results with documented tolerances and revise tests, measures, thresholds, or interventions when warranted.
Keep an auditable record of the intended use, identified risks, measurement definitions, test conditions, baselines, limitations, production findings, incidents, decisions, and follow-up actions. This makes trend interpretation and review more useful, especially when the system or evaluation method changes. NIST’s AI RMF 1.0 is being revised; the framework and its Playbook are voluntary guidance, not a universal legal requirement or a checklist that every team must follow in full. Applicable obligations can depend on deployment and jurisdiction.
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Monitoring can combine internal processes, evaluation methods, and software tools. Compare options against the system’s needs rather than assuming a platform covers every risk:
- Does it cover the material harm categories and performance requirements identified for this use?
- Can it evaluate representative deployment conditions and detect the failures or drift that matter?
- How quickly can it surface a problem, and can the responsible team act on an alert?
- Does it preserve methods, test conditions, results, and incident records for review?
- What data does it collect or retain, and how does that affect privacy and data handling?
- Can the operating team support human intervention, system modification, or safe shutdown when needed?
NIST’s AI Resource Center provides access to AI testing and evaluation guidance and software tools. Its materials do not establish that a particular commercial monitoring platform is validated or best for a given deployment.
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