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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsMonitor an AI system after launch by running a continuing safety loop: define the risks in its real use context, collect signals from the system and the people affected by it, review those signals on a risk-appropriate schedule, and connect findings to investigation and corrective action. Pre-release tests are useful, but they cannot fully represent changing inputs, users, and conditions in deployment. Monitoring is not proof that a system is safe; it is how you detect problems, understand their impact, and respond.
What post-launch safety monitoring needs to cover
A dashboard that tracks only model accuracy or uptime can miss harms that emerge from how people use a system, how it is integrated into a service, or how its outputs affect people at scale. NIST AI 800-4, published in March 2026, groups deployed-AI monitoring into six dimensions. These are lenses for choosing relevant checks, not a universal dashboard that every system must copy.
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| Dimension | Question to ask | Examples of evidence to review |
|---|---|---|
| Functionality | Does the system still perform its intended task within its operating limits? | Task success and error patterns, unexpected outputs, and performance changes. |
| Operations | Is the service dependable, and can operational incidents be connected to behavior? | Availability and relevant infrastructure signals, with enough context to relate outages or changes to model behavior. |
| Human factors | Are outputs understandable and useful in the context 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 evidence from testing defenses at a cadence suited to the risk. |
| Compliance | Are applicable legal requirements and internal controls still being met? | Monitoring records aligned with the system’s actual classification, jurisdiction, and obligations. |
| Large-scale impacts | Are there effects on people or communities that task-level measures do not reveal? | Qualitative and quantitative reviews selected for the system’s context and potential effects. |
NIST describes post-deployment monitoring as including incident monitoring and field studies, because AI systems can vary and behave unpredictably in use. Its 2026 report also identifies unresolved questions about who should monitor, what to monitor, when and why to monitor, and how to combine automated with human review. There is no established review interval or single metric set that fits every deployment.
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1. Define intended use, foreseeable misuse, and harm
Write down the people and groups affected, the contexts in which the system will be used, plausible misuse, and the failure modes that matter. For each failure, describe the potential harm and set operating limits and risk tolerances before choosing a metric. A change in deployment context can change which risks deserve attention, even if the underlying model has not changed.
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NIST’s Generative AI Profile recommends assessing acceptable risk and performance measures against organizational risk tolerance. It also recommends retraining or decommissioning models that fall outside defined limits.
2. Select evidence for each relevant monitoring dimension
Choose measures and review methods that can detect the failures you identified. Pair automated telemetry with structured reviews of samples and feedback from users and operators. For generative AI, NIST AI 600-1 recommends feedback channels, active learning to identify failures or unexpected outputs, and processes for tracking errors and near misses.
Do not treat a rise in alerts as proof of harm, or a quiet dashboard as proof of safety. A signal is a reason to assess the evidence; a confirmed incident requires investigation. Record how a signal is interpreted and what action follows, so that monitoring can be evaluated and improved rather than becoming a pile of unreviewed data.
3. Assign owners, review cadence, thresholds, and authority
Name the person or team responsible for reviewing each signal, the review schedule, the threshold that triggers investigation, and who has authority to pause or roll back a release. Make it possible for affected users and frontline staff to report problems, and route those reports to someone empowered to act.
Set cadence according to the system’s risk, rate of change, and available evidence. For example, a material model or data change may call for added review, while stable operations may support a different schedule. This is a governance choice, not a universal interval prescribed by NIST. Define in advance whether a threshold leads to more sampling, human review, feature restrictions, rollback, suspension, or decommissioning.
4. Preserve enough context to investigate
When a signal warrants review, the team should be able to reconstruct what happened and which system version was involved. For an AI system, useful incident records commonly connect the event to relevant model, data, prompt, configuration, and deployment versions, as applicable. Also retain the time and context of the event, the signal or report that raised concern, the assessment, affected users or groups where known, decisions made, and follow-up actions.
Collect only information needed for the monitoring purpose. Restrict access and set retention rules with applicable law and the context of use in mind; there is no general retention period for all AI systems. These controls help balance investigation needs with privacy and security.
5. Investigate, correct, and learn
For a suspected safety problem, preserve relevant evidence, establish a timeline, identify the system and deployment context, and assess who may have been affected. Determine whether the issue arose from model behavior, data, integration, operating conditions, user interaction, or a combination. Record the root-cause assessment, corrective action, and how the team will check whether the correction worked.
NIST AI 600-1 recommends incident response and recovery, communication with relevant AI actors, after-action assessment, and postmortem analysis. Use those steps to update monitoring, operating limits, training, or release controls. If risk cannot be brought within acceptable limits, restrict or suspend use, or decommission the system.
What to do when monitoring finds a problem
A monitoring process is useful only if it links detection to a response. Establish an escalation path before launch, and distinguish a signal that needs investigation from a confirmed failure. A practical response path is:
- Triage the signal. Confirm what was observed, whether it is reproducible, and whether it may indicate harm or a breach of operating limits.
- Contain immediate risk. Increase human oversight, limit a feature or use case, roll back a change, or suspend use when the potential harm warrants it.
- Assess impact and cause. Identify affected people or groups where possible, preserve evidence, and determine what conditions or versions contributed.
- Notify the right people. Follow internal escalation procedures and any applicable duties to inform users, providers, regulators, or other relevant actors.
- Correct and verify. Make a change, test whether it addresses the problem, and continue monitoring for recurrence or unintended effects.
- Document and learn. Keep the incident record and postmortem, then update controls, thresholds, and monitoring methods as needed.
These steps reflect the response and learning practices recommended in NIST’s Generative AI Profile; the precise response depends on the system and the risk involved.
EU AI Act duties apply to specified high-risk systems
The EU AI Act requirements below are scoped to high-risk AI systems and the relevant provider or deployer role; they are not universal obligations for every AI system. The European Commission AI Act Service Desk displays Articles 26, 72, and 73 using the consolidated regulation text as of 27 July 2026. Its article summaries are non-binding, so organizations should consult the regulation and competent-authority guidance for compliance decisions.
Providers: post-market monitoring
For high-risk systems, providers must establish and document a proportionate post-market monitoring system and plan, and collect, document, and analyze relevant performance data throughout the system’s lifetime. The requirements also address interaction with other AI systems where relevant.
Deployers: operation, logs, and risk response
Deployers of high-risk systems must monitor operation in accordance with the instructions for use. Under Article 26, if a deployer has reason to consider that use may present a specified risk, it must notify the provider or distributor and market-surveillance authority without undue delay and suspend use. The article also requires immediate notification of identified serious incidents through the stated chain.
Automatically generated logs under a deployer’s control must be kept for an appropriate period of at least six months, unless applicable law provides otherwise. This is a specific high-risk deployer requirement, not a general log-retention rule for all AI systems.
Serious-incident reporting periods
Article 73’s outer reporting limits depend on the circumstances: generally, reporting is required immediately after establishing or reasonably suspecting a causal link and no later than 15 days after awareness. Certain widespread infringements or specified incidents have a two-day outer limit, while a death-related incident has a ten-day outer limit. The article also requires investigation, risk assessment, and corrective action after reporting. These deadlines should not be generalized beyond the provision’s scope.
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Article 72 says the Commission shall adopt guidance and a template for post-market monitoring plans by 2 September 2027. That date concerns the Commission’s guidance and template, not a blanket deadline for organizations to begin monitoring.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to judge whether a monitoring setup is useful
Whether a team uses in-house tools, an AI observability service, or a combination, evaluate the setup by whether it can support the work rather than by the number of charts or alerts it produces. Useful selection questions include:
- Which of the six monitoring dimensions does it support, and which still require a separate process?
- Can a signal be tied to the relevant model, data, prompt, and deployment versions?
- How quickly can it detect and route a problem, and what review effort or cost does that require?
- Can human reviewers assess samples well, and can users and operators report problems?
- Does it support triage, escalation, rollback, incident records, and an audit trail?
- Can access, privacy, and retention be configured for the system’s context and applicable law?
- Does the process fit the legal regime that actually applies to the system?
These criteria are decision aids, not a ranking of vendors or a guarantee that any monitoring product will make a system safe. The organization deploying the system still needs clear ownership and authority to respond to findings.
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