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An effective AI incident report is a time-stamped, evidence-backed record of what happened, which AI system was involved, who or what was affected, how the team responded, and what will prevent a recurrence. It should distinguish confirmed facts from assumptions, record unknowns instead of guessing, and preserve sensitive evidence under appropriate access controls.
Use the checklist below for incidents, suspected incidents, and near misses. It supports investigation and governance but does not replace a country- or sector-specific legal, regulatory, safety, privacy, security, or contractual notification duty.
What an AI incident report is meant to do
A report should let a person who was not present reconstruct the event and make sound decisions about safety, support, escalation, remediation, and closure. That requires more than a narrative. The record needs identity, chronology, system context, impact, evidence, actions, ownership, and follow-up criteria.
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Write in language that separates observation from interpretation. “The classifier returned a high-risk label at 14:03 UTC” is an observed fact. “The model caused the customer’s loss” is a causal conclusion that may require investigation. If causation is not established, describe the AI as directly involved, contributory, or present during the event only to the extent the evidence supports.
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The OECD’s 2025 common reporting framework is designed to complement, not replace, national incident-reporting frameworks. It selected 29 criteria, including seven mandatory criteria within its proposed framework and 22 optional criteria. Those numbers describe the framework’s structure, not the frequency or seriousness of incidents.
Incident, hazard, and near miss: use the right status
An incident is an event, circumstance, or series of events in which developing, deploying, using, or malfunctioning an AI system directly or indirectly results in specified actual harm. Harm can include injury or health effects, disruption to critical infrastructure, violations of fundamental, labour, or intellectual-property rights, and damage to property, communities, or the environment.
A hazard is a plausible path to that harm before the harm occurs. A near miss belongs in the reporting system when the potential consequence was credible, even if no injury, loss, or rights violation is confirmed. Label it as a hazard or near miss, state what nearly happened, and avoid presenting an untested causal theory as fact.
Keep the status explicit: for example, “hazard/near miss,” “incident under investigation,” “confirmed incident,” or “closed.” A report can change status as evidence arrives; preserve the previous versions and the dates of each change.
The 12-part AI incident report checklist
1. Report identity and current status
Start with a unique internal reference that can be used in tickets, evidence repositories, meeting notes, and notifications. Add a short descriptive title, the date and time the report was created, the reporting role or team, the current status, and a version history. Record the time zone for every timestamp.
- Reference number and concise title
- Reporter’s role, team, and contact route
- Created date, last-updated date, and time zone
- Status: hazard/near miss, under investigation, confirmed, or closed
- Version number and a brief change log
2. Event timeline
Build a chronological account from the earliest relevant signal through detection, escalation, containment, recovery, and review. Include when the event began, when it ended or was detected, when people were notified, and the time of each significant decision and action. State what remains unknown rather than filling gaps with estimates.
Use a consistent clock, preferably UTC plus the local time when it matters operationally. Preserve the original timestamp from logs alongside any converted display time so investigators can reconcile systems with different clock settings.
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3. AI system and relationship to the event
Identify the product or service, model and provider where known, model or deployment version, affected feature, and downstream workflow. Describe the intended task and the actual use at the time. Record the level of autonomy, the human-oversight arrangement, and any point where a person could review, override, or stop an action.
Assess the relationship to the event using evidence: did the AI directly produce the harmful output or action, contribute to a chain of failures, or merely operate in the environment while another component caused the problem? Keep that assessment provisional until the investigation supports a conclusion.
4. Deployment context
Describe the organization and operational setting, sector, country or region, intended users, and affected stakeholders. Add the scale or exposure that can be supported: number of decisions, accounts, devices, workers, patients, transactions, or hours. Note whether the system was modified, configured differently, connected to a new tool, or used outside its intended conditions.
Context matters because the same output can carry different consequences in a consumer recommendation, a workplace decision, a medical workflow, or critical infrastructure. Record the relevant jurisdiction and sector early so the notification owner can check applicable duties.
5. Inputs, model, task, and outputs
Capture the relevant input or data source and time window, model type and version, material changes, task performed, output generated, and any action taken from that output. Include confidence scores, uncertainty indicators, prompts, configuration, retrieval results, tool calls, and policy settings when they are available and safe to preserve.
Do not copy sensitive personal, security, or proprietary data into an unrestricted report. Put the original prompt, output, or dataset in an access-controlled evidence store and link to its record, with the access decision and retention period documented.
6. Harm, severity, and scope
Separate actual, suspected, and potential harm. For each, state the type of harm, affected people or groups, assets, services, or environment, severity, duration, and quantified scope where supportable. Identify whether critical services or protected rights may be implicated.
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Use “unknown” when a value has not been established; do not use zero to mean “not yet measured.” If the impact estimate changes, retain the earlier estimate and explain what new evidence changed it. Include whether people received support, were exposed repeatedly, or remain at risk.
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List the evidence that supports every material assertion: source-system logs, output snapshots, alerts, tickets, records, relevant dates, witness or submitter information, and any chain-of-custody or access history needed for legal, security, or safety review.
Mark each important statement as an observed fact, a third-party account, a working hypothesis, or a confirmed conclusion. State the confidence level and unresolved questions. Preserve privacy, security, intellectual-property, and legal restrictions while ensuring authorized investigators can retrieve the underlying material.
8. Containment and recovery
Record each protective action with its owner and timestamp. Examples include pausing a model or feature, rolling back a deployment, restricting an account or workflow, adding human review, disabling a tool connection, increasing monitoring, or switching to a safe fallback.
Document customer, worker, patient, or user support; the restoration status; residual exposure; and the conditions for returning the system to normal operation. If a rollback was incomplete or only reduced risk, say so explicitly.
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9. Causes and contributing factors
Describe known or suspected technical, data, process, human, vendor, and organizational factors. State the investigation method used, the confidence in each finding, and the questions still open. A faulty data transformation, an untested model update, an unclear approval path, and an operator’s reasonable action can all contribute without any one factor being the sole root cause.
Avoid assigning “root cause” in the first draft. Use a provisional cause statement, preserve competing explanations, and update the report when testing or additional records resolve them.
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10. Notifications and obligations
Record internal escalation, affected-party communications, provider or vendor contacts, regulators or authorities where required, the reporting channel, the deadline, and the person responsible. Note the basis for the decision to notify or not notify.
Whether a regulator must be contacted depends on the specific country, sector, system, event, contract, and harm. The checklist cannot determine that obligation by itself. Route the question to qualified legal, privacy, safety, security, or compliance staff and preserve their decision and date.
11. Corrective and preventive actions
Turn findings into owned actions rather than general promises. For every action, record the accountable owner, due date, verification evidence, residual risk, and closure criteria. Actions may involve testing, monitoring, data quality, model limits, access controls, human review, governance, training, vendor management, or deployment architecture.
An action is not complete merely because code was changed. Define how the team will demonstrate that the control works, under which conditions it will be checked, and who can accept the remaining risk.
12. Learning and disclosure
Capture lessons that apply to similar systems, products, or locations. Decide whether a de-identified summary can be shared, what restrictions apply, and which approvals are needed. Set a review date so new evidence, repeat events, or changes in law can update the assessment.
How to assemble the report after detection
- Open a controlled record. Assign the reference number, restrict access to the appropriate response group, and record the initial status and reporter.
- Preserve volatile evidence. Export relevant logs, prompts, outputs, configuration, alerts, and access records before routine retention or automated deletion removes them. Hash or otherwise track copies when chain of custody matters.
- Make the system safe. Apply proportionate containment, record the exact time and owner, and state what exposure remains.
- Write the first timeline. Use observed timestamps and identify gaps. Do not wait for a complete causal explanation before documenting known facts.
- Assess impact in three bands. List actual, suspected, and potential harm separately, with affected stakeholders and unknowns.
- Escalate and check duties. Notify the designated internal functions and ask qualified staff to evaluate country-, sector-, contract-, privacy-, safety-, and security-specific requirements.
- Investigate and revise. Test competing explanations, document confidence, and update the model, data, process, and human factors sections as evidence improves.
- Assign and verify prevention. Add owners, deadlines, evidence, residual risk, and closure criteria for every corrective action.
- Close deliberately. Obtain the required approval, record the closure date and rationale, preserve the final version and evidence links, and schedule the learning review.
Use an evidence-and-confidence table
A compact evidence table prevents a persuasive narrative from outrunning what the records show.
| Claim or question | Evidence | Classification | Confidence and next check |
|---|---|---|---|
| What output was returned? | Immutable output snapshot and request log | Observed fact | High; verify timestamp alignment |
| Who received it? | Workflow and notification records | Observed fact or third-party account | State gaps; reconcile duplicate records |
| Did the output cause harm? | Impact records, interviews, and system traces | Hypothesis until tested | Specify the test or missing evidence |
| How many people were affected? | Query definition and validated counts | Measured estimate | Include the time window and uncertainty |
Choosing a reporting scheme or template
If your organization has several forms or frameworks, compare them before an incident occurs. The useful differences are operational, not cosmetic.
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| Comparison area | Questions to ask |
|---|---|
| Jurisdiction and sector | Does it fit the countries, industry rules, contracts, and protected activities involved? |
| Incident definition | Does it distinguish actual harm, suspected harm, hazards, and near misses? |
| Required fields | Which fields are mandatory, which are optional, and who can approve an exception? |
| Impact treatment | Can it record harm type, severity, duration, affected stakeholders, and unknown values? |
| Technical detail | Does it capture data, model, task, output, version, autonomy, and human oversight? |
| Information controls | Can sensitive evidence be linked without exposing it, with retention and access history? |
| Escalation | Are owners, channels, deadlines, and notification decisions recorded? |
| Comparability | Can reports be compared across systems and locations without erasing local legal context? |
The OECD framework emphasizes interoperability while allowing adaptation to domestic legal frameworks. During its development, 88 candidate criteria were grouped into eight dimensions: incident metadata; harm details; people and planet; economic context; data and input; AI model; task and output; and other incident information.
How NIST guidance fits
NIST’s AI Risk Management Framework Playbook offers voluntary prompts under Govern, Map, Measure, and Manage. It is useful for deciding what evidence, controls, and follow-up questions belong in a report, but NIST states that the Playbook is “neither a checklist nor set of steps to be followed in its entirety.” Use it as a source of prompts, not as proof that a report satisfies a particular legal duty. NIST also notes that the AI RMF 1.0 is being updated, so record the edition or page version your team used.
Common reporting failures and fixes
- Only describing the model error: Add deployment context, affected stakeholders, harm, and downstream actions.
- Writing a seamless story from incomplete evidence: Label facts, accounts, hypotheses, and conclusions separately.
- Calling every alert an incident: Classify hazards and near misses accurately while recording the potential harm.
- Putting personal or proprietary data in the main form: Store it in an access-controlled location and link to the evidence record.
- Using zero for unknown impact: Mark unknown, define the measurement method, and update the estimate as records arrive.
- Declaring root cause too early: Preserve competing explanations and document the investigation method and confidence.
- Listing remediation without ownership: Add an owner, due date, verification evidence, residual risk, and closure test.
- Assuming one framework answers notification questions: Have qualified personnel check the event against the applicable country, sector, contract, and regulator requirements.
A practical minimum record
For a small team, the following sequence is a defensible starting point: reference and status; timeline; system and deployment context; inputs, model, task, and output; actual, suspected, and potential harm; evidence and confidence; containment and recovery; causes and open questions; notifications; corrective actions; and learning review. Expand the record when the event affects protected rights, critical services, multiple jurisdictions, or a large population.
An AI incident report is complete when another authorized team can understand the event, verify the important claims, see who made each decision, determine what risk remains, and identify the evidence that will show whether prevention worked. Keep it versioned and update it when new facts change the assessment.
Frequently Asked Questions
Should a near miss be recorded if nobody was harmed?
Yes. Record it as a hazard or near miss, describe the credible potential harm and what prevented it, and keep causal claims provisional.
What should we do when the number of affected people is unknown?
Write “unknown,” define the time window and counting method, preserve the query or records used, and update the estimate as evidence improves.
Does every AI incident have to be reported to a regulator?
Not necessarily. The duty depends on the country, sector, system, contract, event, and harm. Have qualified legal, privacy, safety, security, or compliance staff make and document the determination.
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Only when the form has appropriate access controls. Otherwise store the material in a restricted evidence repository and link to it from the report.
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