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An AI incident is an AI-related event, error, malfunction or control failure that causes—or could plausibly cause—material harm, compromise information or system security, violate an applicable policy, or disrupt safe and trustworthy operations. That is a practical business definition, not a universal legal definition. A harmful outcome is not required: a credible near miss can reveal a weakness that should be captured and escalated.
Use this guide to classify events, separate internal response from statutory reporting, assess severity, and run a defensible response. Legal duties depend on the jurisdiction, the AI system’s category, your organization’s role and the consequences of the event.
Start with a working definition
NIST describes an AI system as an engineered or machine-based system that can generate predictions, recommendations or decisions that influence real or virtual environments. Its AI Risk Management Framework (AI RMF) is a voluntary resource for managing risks through design, development, deployment, use and evaluation; NIST says the AI RMF 1.0 is being revised.
For an incident register, treat an event as an AI incident when an AI system or an AI-enabled process is a material contributor to one or more of these conditions:
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- People, rights, health, safety, property or the environment are harmed or put at credible risk.
- A recommendation, classification, prediction or decision is materially wrong in a way that affects a person, customer, employee or business operation.
- Confidentiality, integrity or availability of information or systems is compromised, or an acceptable-use or security policy is violated or imminently threatened.
- An AI-enabled workflow disrupts an essential operation, produces unsafe behavior or defeats a required control.
- A near miss exposes a credible weakness that could cause material harm if the same conditions recur.
The definition is intentionally broader than a cybersecurity breach. NIST’s incident glossary covers occurrences that actually or potentially jeopardize information-system confidentiality, integrity or availability, or violate or imminently threaten security or acceptable-use policies. The AI RMF applies a wider organizational lens to individuals, organizations, society and the environment across the AI lifecycle.
Why an AI incident is not the same as a reportable incident
Keep three decisions separate:
- Internal capture and escalation: Should the event or near miss be entered in your AI or operational incident system and sent to an accountable owner?
- Business risk response: What must be contained, investigated, corrected or communicated to keep people and operations safe?
- External reporting: Does a law, regulator, contract, privacy rule, safety regime or customer agreement require a notification?
A model error, user complaint, policy breach, cyber event or business loss may justify internal tracking without meeting a statutory “serious incident” test. Conversely, an event that initially appears small may trigger a reporting duty when its consequences, affected population, system category or legal role are understood. Do not use your internal label as a substitute for a jurisdiction-specific legal assessment.
Events that commonly belong in an AI incident taxonomy
The following categories are practical examples, not an official or exhaustive taxonomy. Record the AI system’s contribution and the resulting or plausible consequence rather than assuming that every bad output is an incident.
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Materially wrong recommendations or decisions
An AI tool may recommend denying a benefit, flag a legitimate transaction as fraud, route a patient or customer incorrectly, or rank an applicant using materially inaccurate information. Escalation depends on the effect and the credibility of recurrence. A harmless typo in a draft is normally a quality issue; a wrong eligibility decision that changes a person’s access to a service is a potential incident.
Unsafe behavior or physical harm
An AI-enabled component can contribute to death, serious health damage or dangerous operation. Preserve the operating conditions, human actions, sensor or input data, model and version, and every available override record. Do not wait for a final causal conclusion before protecting people and pausing an unsafe function.
Privacy, confidentiality and security compromise
Examples include sensitive data being exposed in an output, a prompt or retrieval connector sending information to an unauthorized service, a model leaking training or customer data, a compromised model endpoint, or an AI agent taking an unapproved action. Apply your existing security, privacy and breach procedures in parallel with the AI investigation.
Operational disruption and loss of control
An automated workflow may repeatedly misroute work, create a backlog, consume a shared resource, alter records, or make a human review queue unusable. A service outage is an AI incident when the AI component materially contributes to the outage or defeats a control that should have prevented it.
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Policy, governance or acceptable-use violations
Using an unapproved model for confidential work, bypassing required human review, deploying a system outside its authorized purpose, or allowing an agent to access prohibited tools can be an incident even before measurable harm occurs. The control failure is itself important evidence.
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Near misses and credible failure modes
A reviewer who catches a dangerous recommendation before it reaches a customer has found a near miss, not “nothing.” Capture the input, output, control that caught it, and the conditions under which the control could fail. NIST calls for post-deployment monitoring that collects and evaluates input and tracks and responds to incidents and errors.
What does not automatically make an event reportable
These events can require investigation but do not, by themselves, prove a legal reporting obligation:
- A hallucinated or inaccurate answer with no material consequence.
- A bias complaint before the affected decision, scope and legal context are established.
- A near miss that reveals a weakness but caused no covered consequence.
- A policy breach or unauthorized use that is contained internally.
- A cybersecurity event that does not meet the applicable notification threshold.
- A financial or operational loss that is not connected to a statutory, contractual or sector-specific trigger.
For each case, document why it was captured, what harm occurred or could occur, and which rule was considered. Avoid both under-reporting (“no one was hurt, so close it”) and overstatement (“the model was wrong, so it is legally reportable”).
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Use a matrix that records evidence and uncertainty. NIST frames risk as a combination of the likelihood of an event and the magnitude of its consequences. Your thresholds should be approved by the accountable business and risk owners, not improvised by the first person who sees the output.
| Dimension | Questions to record | Why it changes priority |
|---|---|---|
| Impact | What happened or could happen to people, rights, health, safety, property, the environment, information or essential operations? | Direct injury, rights impacts, sensitive-data exposure and safety consequences normally require faster escalation than a reversible quality defect. |
| Likelihood | How plausible is recurrence? Is the trigger repeatable, already occurring or dependent on an unusual combination? | NIST’s likelihood-and-consequence view helps distinguish an isolated low-impact error from a repeatable high-impact failure. |
| Scope | How many people, decisions, customers, systems, locations or records are affected or exposed? | Scale can turn a small individual error into a systemic incident. |
| Duration and reversibility | Is the effect continuing? Can a decision, record or harm be reversed, and at what cost? | Ongoing or irreversible effects justify immediate containment. |
| System and controls | Which model and version, data, prompt or configuration, connected service, human review and override path were involved? | A failed or absent control may require broader corrective action than a one-off input mistake. |
| Duties and roles | Who is the provider, deployer, importer or other responsible party? Which privacy, safety, contractual, sector or AI rules apply? | Legal duties follow the system, role, location and consequence—not merely the word “incident” in an internal ticket. |
Mark unknown facts as unknown, assign an owner to resolve them and preserve the original evidence. A preliminary severity can change as scope, causality or legal applicability becomes clearer.
A practical response procedure
1. Capture and preserve the event
Open an incident or near-miss record as soon as a credible issue is identified. Include:
- Reporter, discovery time, event time and whether the issue is still active.
- Business process, users, locations and decisions involved.
- System name, model and version, deployment environment, prompt or configuration, connected tools and data sources.
- Relevant input and output, subject to privacy, security and retention rules; preserve hashes, logs, screenshots or immutable copies where appropriate.
- Observed impact, plausible worst case, affected people and current safeguards.
- Actions already taken and the people or teams notified.
Do not “clean up” the only copy of a prompt, log or output while trying to fix the system. Restrict access to sensitive evidence and follow your legal-hold and retention requirements.
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Apply the matrix above. Ask whether a vulnerable group, safety-critical process, fundamental right, confidential dataset or essential service is involved. Separate confirmed facts from assumptions. If the issue may be ongoing, treat containment as urgent while the investigation continues.
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3. Contain safely
Choose the least disruptive control that reliably prevents further harm:
- Pause automated decisions or route every case to qualified human review.
- Disable a feature, connector, tool permission, model version or data source.
- Revoke credentials, isolate an endpoint or block a malicious prompt or input path.
- Apply an approved override or fallback process and tell operators exactly when to use it.
- Preserve evidence before changes that could obscure the cause, unless immediate safety requires otherwise.
Containment is not a conclusion that the AI was solely responsible. It is a temporary risk control.
4. Escalate to accountable functions
Notify the business owner and AI or system owner under your response plan. Involve security for confidentiality, integrity or availability concerns; privacy for personal data; legal or compliance for reporting and contractual duties; safety or quality teams for physical or regulated harms; and the vendor or provider when a supplied component may be involved. Record who accepted each decision and the time it was made.
5. Investigate and communicate
Reconstruct the chain from input and configuration through model behavior, human review, downstream action and consequence. Test whether the result can be reproduced, whether a data or prompt change explains it, and whether another control should have caught it. Do not assume that a plausible explanation proves causation.
Communicate appropriate facts, uncertainty, mitigations and next steps to relevant AI actors, affected customers or communities, regulators and contractual partners. Coordinate messages with privacy, legal, safety and communications owners; avoid exposing sensitive personal data or security details unnecessarily.
6. Recover and learn
Restore service only after the accountable owner confirms that controls, monitoring and rollback paths are adequate. Correct or retrain the affected workflow where justified, update prompts and access controls, review similar systems and users, and record residual risk. NIST AI RMF Core Manage 4.3 states: “Processes for tracking, responding to, and recovering from incidents and errors are followed and documented.”
Evidence and questions for the investigation
A technically sophisticated investigation can still fail if it cannot show what the system was allowed to do at the time. Preserve:
- Model, application and policy versions, including changes immediately before the event.
- Input data lineage, retrieval results, prompts, system instructions and relevant user actions.
- Confidence or ranking information, filters, guardrails, human approvals and override attempts.
- Tool calls, API responses, access logs, alerts, queue states and downstream records.
- Sampling or monitoring data that shows whether other cases were affected.
- Decisions about severity, containment, notification, recovery and residual risk.
Ask: What did the AI actually do? What did people and connected systems do afterward? Which control failed, was missing or was bypassed? Could the result be reproduced? How many cases share the same conditions? What evidence supports or contradicts a causal link? Which affected people need correction, appeal or redress?
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EU AI Act example: a specific legal test
The European Union’s AI Act illustrates why a broad internal definition cannot be substituted for a legal threshold. Article 3’s serious-incident definition covers an incident or malfunctioning of an AI system that directly or indirectly leads to specified outcomes, including death or serious damage to health; serious and irreversible disruption of critical infrastructure; breach of obligations under Union law intended to protect fundamental rights; or serious damage to property or the environment.
Article 73’s reporting rule applies to providers of high-risk AI systems placed on the Union market. The provider reports covered serious incidents to the relevant market-surveillance authority after establishing a causal link or reasonable likelihood. The timing depends on the category:
| EU AI Act situation | Deadline described in Article 73 |
|---|---|
| Covered serious incident generally | Immediately after a causal link or reasonable likelihood is established, and no later than 15 days after awareness. |
| Widespread infringement or the specified serious-incident category | Immediately and no later than two days after awareness. |
| Death | Immediately once a causal relationship is established or suspected, and no later than 10 days after awareness. |
Initial reports may be incomplete and followed by complete reports. The provider must investigate, assess risk, take corrective action and cooperate with authorities. Article 75 provides a route for cases under the AI Office’s competence, so confirm which authority applies. The Commission’s draft guidance and reporting-template consultation, published in September 2025, described an August 2026 application of the reporting obligation. Dates, amendments and guidance can change; verify the current consolidated Act, authority and sector rules before relying on a deadline.
This EU example is not a universal rule. It identifies a duty for providers of covered high-risk systems, while deployers and other organizations may have different obligations. A company should still capture and manage a near miss even when Article 73 does not apply.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Design an incident policy before the first event
A usable policy turns judgment into repeatable action. Define:
- Which AI systems, experiments, vendors, agents and business processes are in scope.
- What counts as an incident, near miss, complaint, vulnerability, change and routine quality issue.
- Severity levels, decision rights, response times and an on-call route.
- Required evidence, privacy safeguards, retention and legal-hold procedures.
- Human-review, appeal, override, rollback and decommissioning criteria.
- Who can pause a system and who can authorize recovery.
- When security, privacy, safety, legal, regulators, customers and affected communities are notified.
- How corrective actions are tracked and how lessons are shared across similar systems.
NIST AI RMF Core Manage 4.1 calls for post-deployment monitoring plans that include mechanisms for user and other relevant actor input, appeal and override, decommissioning, incident response, recovery and change management. Treat monitoring and feedback as operating controls, not as a one-time launch checklist.
Worked classification examples
Incorrect internal summarization
An assistant omits a noncritical detail from a meeting summary that a colleague corrects before use. Log it as a quality event if it reveals a recurring weakness. Escalate as an AI incident if the omission could materially mislead a customer, alter a regulated record or bypass a required review.
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A model rejects a small number of transactions, all are restored the same day and no customer loses money. Capture the event and assess scope, recurrence, affected customers and the appeal path. It may be a moderate incident or near miss; the label depends on impact and controls, not simply on the model’s error rate.
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Agent exposes confidential records
An agent uses an overly broad retrieval permission and returns another customer’s records. Immediately restrict access, preserve logs, involve security and privacy, determine the population exposed and follow applicable breach and contractual procedures. The event is both an AI incident and a potential information-security or privacy incident.
Safety intervention catches a dangerous recommendation
A human reviewer rejects an AI-generated instruction that could have damaged equipment, and no harm occurs. Record the near miss, identify why the output was produced and whether the reviewer and override path were consistently available. Test similar prompts and versions before resuming automation.
Key questions businesses ask
Does an AI incident require actual harm?
No. A credible near miss or control weakness belongs in internal monitoring when it could plausibly cause material harm. Actual impact remains important for severity and legal analysis.
Is every hallucination an incident?
No. Evaluate the use case, audience, consequence, scale, reversibility and controls. A harmless draft error is different from an unsupported medical, safety, eligibility or financial decision.
Who is responsible when several systems contribute?
Investigate the whole chain. Record the model, prompts, data, human decisions, connected services and operational controls. Responsibility and reporting duties depend on applicable law and contractual roles; do not assign sole causation to the model without evidence.
Does NIST impose a reporting deadline?
No. The AI RMF is voluntary guidance. It supports monitoring, response, recovery, documentation, communication and change management but does not create a universal incident-reporting deadline.
Where can a team find the relevant NIST controls?
Use the NIST AI RMF Core for Manage outcomes, and the AI RMF 1.0 executive summary for the framework’s system and risk-management context.
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Record an AI-related event when it causes or could credibly cause material harm, security or privacy compromise, policy violation or unsafe operational disruption. Triage consequence and likelihood in context, contain first when people or systems may be at risk, preserve evidence, involve the right functions and keep internal escalation separate from external legal reporting. Then verify the current law, authority, role and deadline before deciding whether a notification is required.
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