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Monitor an AI system by first defining the harms it could cause in its actual use, then measuring those risks and ordinary performance against documented baselines. In production, watch for both unsafe behavior and changes in quality, route alerts to people with authority to act, and reassess the measures when the system or its operating context changes. There is no single metric or universal evaluation schedule that suits every AI system.
What to monitor: safety and performance
Harmful outputs and performance drift are related but different problems. A system can produce unsafe responses while its average accuracy looks stable; it can also become less accurate without generating content that is obviously harmful. A monitoring plan should therefore cover both the outcomes people experience and the reliability of the system and its components.
Start with the system’s intended use and deployment conditions. For example, an AI assistant answering questions about a financial product raises different concerns from a model that flags transactions for fraud review or helps prioritize loan applications. Identify who may be affected, how decisions or outputs are used, and what can happen if the system is wrong. Do not assume that every trustworthiness characteristic matters equally in every setting.
NIST’s AI Risk Management Framework (AI RMF) and its Generative AI Profile offer voluntary guidance for this work. NIST’s AI Resource Center describes AI RMF 1.0 as being revised; the Playbook presents suggestions, not a universal checklist or mandatory sequence. These materials do not prescribe one monitoring interval or make a particular metric appropriate for every deployment.
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1. Define the context and material harms
Write down the system’s purpose, users, affected people or groups, components, and expected operating conditions. Include how people rely on its output: whether it is informational, used to recommend an action, or can trigger an action automatically. This makes it possible to distinguish a harmless error from one that could cause financial loss, expose private information, or unfairly affect a person.
List the plausible harms for this context and rank them by severity and likelihood. For a generative system, relevant categories may include harmful bias, privacy violations, offensive or violent content, misleading answers, or assistance with inappropriate, malicious, or illegal requests. For a predictive system, relevant risks might include degraded performance for a population or a rise in incorrect flags. These are examples to assess, not a fixed list that applies to every system.
Use domain expertise, user or community feedback, prior incidents, and near misses to identify what tests might miss. Record risks that matter but cannot currently be measured reliably; absence of a measurable indicator is not evidence that the risk is absent.
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2. Choose measures that connect to those risks
For each high-priority harm, define an observable measure, its data source, who reviews it, and what decision it can inform. Pair safety measures with performance and reliability measures. A single aggregate accuracy score can hide important failures, such as errors concentrated in a particular task or group.
| Monitoring area | Possible measure | What it can reveal |
|---|---|---|
| Output safety | Rate of reviewed outputs that violate a defined safety category, reported with the sample and denominator | Whether harmful responses are appearing or becoming more common in the evaluated traffic |
| Privacy and misuse | Observed privacy incidents or unsafe responses to inappropriate requests and attempts to bypass safeguards | Whether the system exposes sensitive information or handles adversarial and malicious inputs poorly |
| Task quality | Task-specific error rates or other quality measures, including breakdowns relevant to the use case | Whether the system is still performing acceptably for the work it is intended to support |
| Reliability and robustness | Out-of-range performance, response times, system failures, and downtime | Whether the service is becoming less dependable or behaving poorly under operating conditions |
| Impact and response | Incident counts, response times, and relevant reports of harm or near misses | Whether problems are being detected and addressed in time, and whether the controls are effective |
The measures need a defined method: specify what counts as a failure, how outputs are sampled or reviewed, how categories are assigned, and how uncertainty or missing data is handled. For generative systems, some outputs may require human review because an automated evaluator will not reliably capture every context-dependent harm. NIST’s Generative AI Profile states: “Safety metrics reflect system reliability and robustness, real-time monitoring, and response times for AI system failures.”
3. Establish a baseline before release
Evaluate the system before deployment so later results have a reference point. Document the test data, metrics, tools, deployment conditions, performance benchmarks, and known uncertainty. Use scenarios that resemble expected use, including the kinds of users, inputs, workflows, and safeguards the system will encounter. A result from a narrow test set should not be presented as proof of performance in every real-world setting.
Test for known failure modes and plausible changes, including concept drift and high load. Draw on incident history, near misses, and domain experts to select scenarios. Record the conditions tested, where the system failed, and whether it failed safely—for example, by withholding an answer or routing a case for human review where appropriate. Stress tests can expose weaknesses, but passing one does not guarantee safety after release.
4. Monitor production behavior
Production monitoring should cover the system’s relevant behavior and functionality, not just whether the service is online. Track the measures selected for the system’s risks, together with operational signals such as response times, downtime, and out-of-range performance. For a customer-facing financial assistant, this might include reviewed samples of answers about account or product questions. For a model that flags transactions, teams may examine errors and performance changes in the workflow where those flags are acted on.
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Keep enough information to investigate a signal and understand what changed, while handling user data appropriately. Document the monitoring method, test conditions, limitations, results, incidents, and actions taken. When outputs or decisions are reviewed by people, include the relevant review findings rather than relying only on system-generated metrics.
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5. Set a risk-based review cadence
NIST calls for regular evaluation but does not set a universal interval. Choose a cadence based on the potential severity and speed of harm, how quickly the model or its environment can change, how much traffic it handles, and how quickly a problem needs to be detected. A low-impact, stable use may justify a different schedule from a high-impact workflow or a system whose inputs and operating conditions change frequently.
Combine scheduled reviews with event-triggered evaluation. Reassess promptly when there is a model, prompt, data, or workflow change; a significant incident or near miss; a shift in user behavior or input patterns; or evidence that performance has moved outside the organization’s accepted range. The cadence and triggers should be written down so teams know what “regular” means for this deployment.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.6. Define alert thresholds, owners, and interventions
Set thresholds from documented risk tolerances rather than adopting a generic number. Specify who receives each alert, how quickly it must be reviewed, who can pause or change the system, and how a decision is recorded. Thresholds may differ by harm: a serious privacy or safety incident may require escalation even if the aggregate rate of other errors remains within its target.
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Prepare response options before deployment and match them to severity. Depending on the system, actions may include validating the signal, investigating affected outputs or decisions, applying a mitigation, recalibrating or modifying the system, adding human review, or suspending or shutting it down. NIST’s risk and trustworthiness guidance identifies human intervention, system modification, and shutdown as possible controls where appropriate; it does not prescribe one playbook for every system.
Practice the response path and measure its operation, including time to respond and system downtime where relevant. This helps reveal whether escalation reaches someone able to act and whether a proposed intervention works in the actual deployment.
7. Review the program and its tools
At regular reviews, compare observed results and incidents with the documented risk tolerances. Reconsider whether the metrics still represent the harms that matter, whether the tests remain representative, and whether the controls are working. Use production events and new testing to update the measures as knowledge, methods, system behavior, and impacts change.
If evaluating software for testing or production monitoring, compare options on the issues that affect your use case: coverage of material harms, representative test conditions, ability to detect safety failures and drift, alert and response latency, auditability, data handling, and support for human intervention or safe shutdown. NIST’s AI Resource Center provides access to AI testing and evaluation guidance and software tools; the cited NIST materials do not rank commercial platforms or establish that a particular product is suitable for a deployment.
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