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If an AI system injures someone, exposes sensitive data, makes a harmful decision, or creates a credible risk, treat it as an active safety, security, governance and potentially legal incident. Protect people and stop repeat exposure first; appoint one incident owner; preserve the exact system state and timeline; classify actual harm and near misses; map jurisdiction-specific duties; then investigate, notify, remediate and verify that controls work.
What should I do if an AI system harms someone?
The first response is not a debate about whether the model, operator or vendor is to blame. It is a controlled incident response. Keep the affected person safe, prevent the same output or action from reaching more people, preserve evidence before changing the system, and create a decision trail that another investigator can audit.
- Protect people. Stop, pause or constrain the affected workflow. If there is immediate danger, use emergency services, safeguarding procedures or other urgent channels appropriate to the location and harm.
- Prevent repeat exposure. Disable the failing feature, revoke a tool permission, place outputs behind human approval, restrict affected users or roll back to a known-safe configuration. Choose the least destructive control that stops additional harm.
- Appoint one incident owner. The owner coordinates safety, engineering, security, legal, compliance, communications and any vendor contacts. Record every decision with a timestamp, decision-maker, evidence considered and next review time.
- Preserve evidence. Capture prompts, inputs, outputs, model and policy versions, tool calls, access logs, user reports, screenshots, downstream decisions and deployment configuration before routine retention or a software change removes them.
- Start a jurisdiction map. List where the system operated, where people were affected and which rules may apply. Privacy, discrimination, consumer-protection, employment, product-safety, cybersecurity, medical-device, financial-services and critical-infrastructure obligations can apply even when no AI-specific rule does.
Do not erase a harmful output simply because it is embarrassing or unsafe to display broadly. Restrict access, store it securely and preserve an unmodified copy for investigators. If a person is in continuing danger, safety action takes priority over perfect evidence collection; record what had to be changed and why.
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How do I stabilize the first hour?
Protect affected people and critical services
Determine whether anyone faces physical injury, self-harm, harassment, discrimination, loss of essential services, financial loss, privacy exposure or environmental damage. Contact emergency or safeguarding teams for immediate threats. For a safety-critical or public-facing service, switch to a tested manual process if one exists. Tell operators which outputs are unsafe and what decisions they must not make without qualified human review.
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Freeze the relevant state without freezing everything
Place the affected model, prompt chain, retrieval index, tool integration or policy behind a change freeze. A blanket shutdown may create a different risk, such as stopping a medical or industrial process with no safe fallback. Record the scope, start time, owner and exit condition of the freeze. Preserve a read-only snapshot of configuration, permissions, model identifier, policy files, system prompts, retrieval sources and feature flags.
Create a time-stamped decision log
Use a shared incident record with append-only or access-controlled history. Log discovery time, first known occurrence, who reported it, every containment action, notifications, approvals, evidence locations and unresolved questions. Synchronize timestamps to a known time standard and note time zones. This log becomes the backbone of the later timeline and helps separate what was known at each decision from facts discovered afterward.
Preserve the chain of custody
Copy raw logs and files without editing them. Record the collector, collection time, storage location, hash or other integrity check where your organization uses one, and every subsequent access. Keep experiments, red-team probes and regenerated outputs on a controlled copy so that investigative work cannot overwrite the production record.
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Use two labels rather than reserving the word incident for an injury. An incident involves actual harm. A hazard is a potential danger that could cause harm under credible conditions. A near miss belongs in the record even when no one was injured; it may reveal a control failure before a more serious event.
The OECD’s 2025 common reporting framework describes 29 reporting criteria and is designed as a cross-sector benchmark. Use its concepts while adding fields required by your regulator, contract or internal policy.
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Minimum classification record
- First known occurrence, discovery time and whether the condition is ongoing.
- Countries, sites and affected locations.
- System, product, model, policy and software versions.
- Developer, provider, deployer and relevant vendors.
- Severity and status: hazard, incident, serious incident or disaster.
- Harm category: physical, psychological, reputational, economic or property, environmental, public-interest or critical-infrastructure, or human and fundamental-rights harm.
- Number and lawful identity categories of affected stakeholders, including vulnerable groups where relevant.
- Estimated injury, loss, compensation or other quantification, with an explanation of uncertainty.
- Prompts, inputs, outputs, logs, screenshots, tool actions and supporting material.
- Direct cause, contributing factors, failure to act, overreliance, intentional misuse, human error and possible legal non-compliance.
- Containment, notifications, corrective actions and residual risk.
Do not label an event “user error” and stop there. A user may have made the final mistake while misleading interface design, poor access controls, unrealistic automation targets or missing review gates made that mistake likely.
How do I report an AI incident?
Reporting starts with a jurisdiction map, not a single global deadline. Identify the countries where the incident occurred, where affected people reside, where the provider and deployer are established, and where the system performs regulated work. Assign a named owner to each regulator, customer, insurer, affected person and internal approval.
EU high-risk AI systems: Article 73 timing
For an EU high-risk AI system, the provider reports a serious incident to the market-surveillance authority of the member state where it occurred. The normal maximum is 15 days after the provider becomes aware, and the report is made immediately after a causal link, or the reasonable likelihood of one, has been established. A widespread infringement or the specified serious-incident category has a two-day maximum. When a person dies, the maximum is 10 days. These are maximum periods, not a reason to wait; begin notifying as soon as the facts support a report.
An initial report may be incomplete and followed by a complete report. The provider must investigate without delay, perform a risk assessment, take corrective action, cooperate with authorities and avoid altering the system in a way that could affect evaluation before informing authorities. Coordinate containment with legal and technical teams so that safety is protected while the pre-change state remains available for evaluation.
General-purpose models with systemic risk
For a general-purpose AI model with systemic risk, the EU AI Act Service Desk says providers must keep track of, document and report serious incidents and possible corrective measures without undue delay to the AI Office and, where appropriate, national competent authorities. Confirm whether your role is provider, deployer or another actor before assigning the duty; the obligation and information available to each party may differ.
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Other regimes that may apply
AI-specific reporting is only one path. A harmful employment decision may trigger employment and discrimination rules. A privacy exposure may require data-protection notification. A faulty product recommendation, medical-device function, financial decision, cyber event or critical-infrastructure disruption can have sector deadlines that run independently of an AI law. Obtain jurisdiction-specific legal advice, preserve the evidence supporting the advice and record why a decision was made not to notify.
What a regulator or affected organization needs
Send a factual, updateable account: what happened, when, where, who was affected, what the system did, what remains uncertain, what has been contained, what evidence is preserved, and who can answer follow-up questions. Do not speculate about root cause in the first notification. Mark hypotheses as hypotheses and provide the time of the next update.
How should an investigation find the root cause?
Build a timeline from the first model or system action through discovery, containment, notification and recovery. Correlate model logs with application logs, identity records, retrieval events, tool calls, human approvals and downstream decisions. Look for the earliest point at which a control could have prevented or reduced the harm.
Test each layer separately
- Model behavior: hallucination, unsafe refusal, biased classification, prompt injection susceptibility, unstable output or a changed capability after an update.
- Data: poisoned, stale, incomplete or unlawfully obtained training, retrieval or user data; incorrect labels; or leakage of confidential information.
- Retrieval and orchestration: wrong documents, ranking errors, context truncation, prompt-template defects, unsafe agent loops or a tool called with the wrong parameters.
- Access control and security: excessive permissions, stolen credentials, missing tenant isolation, unlogged administrative actions or an attacker deliberately misusing the system.
- Human factors: overreliance, unclear responsibility, inadequate training, workload pressure, misleading confidence cues or a review step that existed on paper but not in practice.
- Deployment context: use outside the validated population, language or geography; automation in a high-stakes setting; missing fallback procedures; or a vendor change not reflected in the risk assessment.
- Monitoring and governance: alerts that were not configured, ignored or routed to nobody; evaluation that omitted the failure mode; weak change management; or a supplier that did not provide required records.
Separate causes from contributing factors and failures to act
A root-cause record should distinguish the direct mechanism from conditions that made it possible. For example, a model may have produced an incorrect eligibility score (direct cause), a stale retrieval index may have supplied obsolete policy (contributing factor), and the organization may have failed to require human approval for borderline cases (failure to act). This distinction produces corrective actions that address the system rather than only retraining one model.
Quantify impact and uncertainty
Count affected people, transactions, locations, duration and repeated exposures. Estimate injury, financial loss, property damage, remediation cost and compensation where possible. State confidence ranges and data gaps instead of presenting an early estimate as a final number. Identify people who may not know they were affected so notification and monitoring can reach them.
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Which containment option should be used?
Choose controls by immediacy of safety action, reversibility, harm severity, confidence in causation, number and vulnerability of affected people, evidence-preservation risk, reporting deadline, transparency needs and residual risk. The following options are not mutually exclusive.
| Control | Use when | Benefit | Trade-off |
|---|---|---|---|
| Pause the feature or system | Harm is severe, ongoing or poorly understood | Stops new exposure quickly | May interrupt essential service; requires a safe fallback |
| Rollback model, prompt or policy | A recent change is a credible trigger and the prior state is available | Reversible and fast | Does not fix an older defect; preserve both states for comparison |
| Restrict users, locations or use cases | Risk is concentrated in a population, geography or workflow | Maintains lower-risk service | Requires accurate scope and can create unequal access |
| Require qualified human approval | Outputs inform high-impact decisions but automation can be slowed | Reduces overreliance and catches edge cases | Reviewers need authority, time, training and a clear escalation path |
| Revoke credentials or tool access | Unauthorized action, prompt injection or excessive permissions are suspected | Limits blast radius | Can break legitimate workflows; rotate and restore deliberately |
| Notify and compensate affected people | People face material risk, loss or rights impact | Supports safety, remedy and trust | Requires accurate audience, language, timing and privacy controls |
Document who approved the selected control, what alternatives were rejected and what residual risk remains. Reassess after each new fact; a temporary workaround can become unsafe if it runs longer than its assumptions allow.
What should an AI incident report contain?
Use this practical structure for an internal report, regulator submission or update to a customer. Adapt terminology and mandatory fields to the applicable authority.
- Identity and status: incident ID, owner, discovery time, current status and next review.
- System context: system and model names, versions, provider, deployer, environment, integrations and geography.
- Description: what happened, how it was detected, first known occurrence and whether it is ongoing.
- Affected parties: people, organizations, customers, workers, public services or infrastructure, including vulnerable groups where lawfully recorded.
- Harm and severity: actual harm, hazard or near miss; category; scope; duration; injury, loss and confidence in estimates.
- Evidence: preserved prompts, inputs, outputs, logs, screenshots, tool actions, configuration snapshots and chain-of-custody details.
- Causal analysis: direct cause, contributing factors, misuse, human error, control failures and unresolved hypotheses.
- Containment: pauses, rollbacks, access changes, human review, safety measures and their start times.
- Notifications: regulators, affected people, customers, insurers, vendors and internal executives; include deadlines, submission dates and contact owners.
- Corrective action: model or policy changes, data fixes, permission changes, compensation, retraining, replacement or retirement decisions.
- Residual risk: what remains possible, who accepted it, monitoring frequency and conditions that trigger escalation.
- Follow-up and closure: tests, monitoring changes, red-team scenarios, lessons learned and objective closure criteria.
How should affected people and customers be informed?
Communication should tell people what they need to protect themselves, not merely defend the organization. Explain the system’s role in plain language, the period and information involved, known and possible effects, immediate steps people can take, how to request human review or correction, available remedies and a contact channel that is staffed.
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What remediation prevents a repeat?
Strengthen monitoring and evaluation
Add tests for the observed failure mode and for plausible neighboring failures. Monitor error rates, refusal behavior, bias indicators, tool-call anomalies, drift, data freshness, human overrides and complaints. Set thresholds that trigger a named response, not alerts that disappear into a dashboard. Re-run evaluations after every model, prompt, retrieval, policy, vendor or infrastructure change that could alter behavior.
Improve access and change management
Use least-privilege credentials, tenant isolation, approval for high-impact tools, credential rotation and logs that operators cannot silently rewrite. Require risk review, rollback plans, test evidence and an owner before production changes. Record vendor releases and contractually require incident notice, relevant logs and cooperation with investigations.
Make human oversight real
Define which decisions require a qualified person, what information that person must see, how much time is available, and when the person must reject or escalate an output. Measure override quality rather than counting approvals. Remove interfaces that imply certainty when the system is probabilistic or outside its validated context.
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NIST describes its AI Risk Management Framework as helping developers, users and evaluators manage risks affecting individuals, organizations, society and the environment. It spans pre-design, design and development, deployment, use, and test and evaluation. NIST’s Generative AI Profile is listed as NIST-AI-600-1, released July 26, 2024; the AI RMF Playbook was released March 30, 2023. Use these lifecycle resources to turn lessons from the incident into requirements, tests, ownership and evidence across the system’s life, rather than treating the patch as the end of the response.
When is an AI incident actually closed?
Closure is a documented decision, not the moment a model is patched. Before closing, verify that containment stopped the observed harm, affected people received required notice or remedy, all reports and updates were submitted, evidence is preserved, and corrective changes passed independent testing in a controlled environment.
- The failure mode no longer reproduces under realistic and adversarial tests.
- Residual risks have an owner, monitoring threshold and escalation route.
- Permissions, prompts, retrieval sources, policies and vendor dependencies match the approved design.
- Follow-up sampling shows that human reviewers can detect and stop unsafe outputs.
- Lessons have been added to evaluations, training, change controls and incident playbooks.
- Closure approval, date, evidence and any continuing obligations are recorded.
Keep the case open or move it to monitored recovery when causation remains uncertain, affected people have not received a remedy, reporting duties are unresolved or corrective controls have not operated long enough to establish confidence.
Quick Recap
What facts should every response team remember?
- Protect people and stop repeat exposure before arguing about blame.
- Preserve the exact system state, logs, prompts, outputs and timeline before making destructive changes.
- Record credible hazards and near misses as well as injuries.
- Map every relevant jurisdiction; EU high-risk AI serious-incident deadlines can be two, 10 or 15 days depending on the event.
- Investigate model, data, tools, access, people, deployment context and monitoring together.
- Close only after notification, remedy, testing, monitoring and residual-risk ownership are complete.
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