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Monitor an AI system after launch by combining performance and security signals with user reports, human review, and evidence about real-world effects. Assign people to investigate those signals, set thresholds that trigger specific actions, and keep a documented path to correction, rollback, suspension, or decommissioning. Pre-launch tests matter, but they cannot reproduce every input or use context the system will encounter in the field.
Build a monitoring plan around the system’s risks
Start by defining what the system is intended to do, who may be affected, and what could go wrong in its actual setting. A useful plan connects each potential failure to its possible harm, a way to detect it, and an owner who can respond. Set operating limits and risk tolerances before choosing metrics; otherwise, a dashboard can show movement without telling the team whether it matters.
- Document intended uses and foreseeable misuse.
- Identify affected people or groups, including those who may not directly use the system.
- List plausible failures and their potential severity.
- Define acceptable operating limits and what conditions require intervention.
NIST’s Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile (NIST AI 600-1, published 26 July 2024) recommends using organizational risk tolerance to assess acceptable risks and performance metrics. It also identifies retraining or decommissioning as possible responses when a model falls outside defined limits.
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NIST AI 800-4 groups post-deployment monitoring into six areas. They are a way to organize coverage, not a universal dashboard specification: choose measures that fit the system’s risk, use, and context.
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| Dimension | Questions to monitor | Possible evidence |
|---|---|---|
| Functionality | Does the system continue to perform its intended task? Are outputs changing or failing in unexpected ways? | Task success, error patterns, unexpected outputs, and performance changes. |
| Operations | Is the service dependable, and can operational problems be connected to model behavior? | Availability and relevant infrastructure signals, recorded with enough context to investigate related behavior. |
| Human factors | Are outputs understandable and useful in the setting where people rely on them? | User feedback, appeals, overrides, and human review where appropriate. |
| Security | Is the system exposed to attacks, misuse, or adversarial inputs? | Security signals and checks of defenses at a cadence suited to the risk. |
| Compliance | Do applicable legal requirements and internal controls continue to be met? | Evidence mapped to the system’s actual classification, jurisdiction, and obligations. |
| Large-scale impacts | Are there downstream effects on people or communities that task-level metrics miss? | Appropriate qualitative and quantitative review, rather than reliance on a single output metric. |
A strong task-success rate, for example, cannot by itself establish that the system is safe: it may not reveal who is affected by errors, whether people can challenge an output, or whether wider impacts are emerging.
Collect signals that can be investigated
Use more than automated telemetry. Combine it, as appropriate, with user and operator reports, structured reviews of output samples, incident records, reports of errors and near misses, and records of changes to models or data. For generative AI, NIST AI 600-1 recommends feedback channels, active learning to find failures or unexpected outputs, tracking errors and near misses, and documenting incident response and postmortems.
Record context as well as the event
To make a signal useful for investigation, keep the information needed to reconstruct what happened. Depending on the system and the monitoring purpose, a record may include:
- When the event occurred and how it was detected or reported.
- The system, model, data, prompt, and deployment versions involved, where applicable.
- The relevant input and output or a privacy-conscious reference to them.
- The use context, affected workflow, and whether a person reviewed, overrode, or acted on the output.
- The suspected failure, observed impact, severity assessment, investigation owner, and response taken.
This is an operational checklist, not a fixed legal schema. Collect only information needed for the stated monitoring purpose, restrict access, and set retention with regard to the system’s context and applicable law. More logs do not prove that a system is safe; logs matter when they support analysis and action.
Set review cadence, ownership, and escalation before launch
For every signal, name who reviews it, how it reaches them, and what threshold starts an investigation. Also identify who has authority to pause or roll back a release, restrict a feature, or suspend use. Make it easy for users and operators to report concerns; a report channel that nobody monitors is not a safety control.
There is no settled review interval for every AI system. NIST AI 800-4 identifies who, what, when, why, and how to monitor—including cadence and the balance of automated and human-validated review—as open questions. Set a schedule based on the deployment’s risk, rate of change, and the speed at which harm could occur. Increase review or sampling after a meaningful model, data, prompt, product, or operating-context change; do not wait for a calendar review if a serious signal arrives.
Connect each threshold to a response, such as investigating the event, increasing sampling, adding human review, restricting a feature, rolling back a change, suspending use, or decommissioning the system. Separate an alert from a confirmed safety failure: an alert is a reason to assess evidence, not proof by itself. Document who decides, what evidence they need, and how that decision is recorded.
Investigate signals and close the loop
- Preserve relevant evidence. Retain the records needed to understand the event, while following applicable privacy, access, and retention rules.
- Reconstruct the event. Establish a timeline, identify the system version and use context, and determine what the system and surrounding workflow did.
- Assess exposure and severity. Identify who may have been affected, whether harm occurred or was narrowly avoided, and whether similar cases may exist.
- Correct and contain. Choose a proportionate action: for example, a workflow change, additional human review, a feature restriction, rollback, suspension, retraining, or decommissioning.
- Communicate and learn. Notify relevant people or teams, document the rationale and corrective action, and use a postmortem or after-action review to improve monitoring and response procedures.
NIST AI 600-1 calls for organizational processes for post-deployment monitoring and highlights incident response, recovery, communication with relevant AI actors, and postmortem analysis. A closed incident should therefore produce more than a ticket marked resolved: the team should decide whether the detection method, threshold, ownership, or operating limit needs to change.
What the EU AI Act requires for high-risk systems
The EU AI Act obligations described here are specific to high-risk AI systems and their providers or deployers; they are not general requirements for every AI system. The European Commission AI Act Service Desk presents Articles 26, 72, and 73 using consolidated text displayed on 27 July 2026. Its article summaries are non-binding; consult the regulation’s provisions and competent-authority guidance for compliance decisions.
Provider post-market monitoring
Providers of high-risk systems must establish and document a proportionate post-market monitoring system and plan. They must collect, document, and analyze relevant performance data throughout the system’s lifetime, including interactions with other AI systems where relevant.
Deployer monitoring, logs, and notification
Deployers of high-risk systems must monitor operation in accordance with the instructions for use. If a deployer has reason to consider that use may present a specified risk, Article 26 directs it to notify the provider or distributor and the market-surveillance authority without undue delay and to suspend use. Identified serious incidents must be notified immediately through the stated chain. Logs automatically generated by the system and under the deployer’s control must be kept for an appropriate period of at least six months, unless applicable law provides otherwise.
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Under Article 73, the outer reporting limit is generally 15 days after awareness, following establishment or reasonable suspicion of a causal link. The limit is two days for specified widespread infringements or incidents and ten days for incidents involving a death. These periods apply to the specified legal circumstances, not to all AI safety reports. Article 73 also requires investigation, risk assessment, and corrective action after reporting.
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Evaluate monitoring tools by the response they enable
AI observability or monitoring software can help collect, inspect, and route operational or model-behavior signals, but a tool is only useful if it fits the monitoring process. When comparing approaches, check whether they can:
- Cover the relevant monitoring dimensions rather than only infrastructure or output metrics.
- Associate signals with model, data, prompt, and deployment versions where applicable.
- Support the detection speed and review cost the risk requires.
- Integrate human review and user reporting with automated alerts.
- Support triage, escalation, rollback, and an audit trail.
- Provide suitable privacy, access, and retention controls.
- Fit the legal regime and evidence obligations that apply to the system.
NIST published its report Challenges to the Monitoring of Deployed AI Systems (AI 800-4) in March 2026, drawing on practitioner workshops and literature review. It describes post-deployment monitoring as crucial because AI behavior can vary and manifest unpredictably, while noting that methods and shared terminology remain immature and scattered. A tool or dashboard cannot resolve those organizational gaps on its own; clear ownership and the ability to act on findings remain essential.
Quick Recap
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