AI Incident Report Template

Report an AI incident or near miss: harm, severity, timeline, root cause, containment, notifications incl. AI Act Art. 73

AI & DataStandarOECD AI incident concepts (2024); EU AI Act Article 73incident reportDiperbarui 20 Okt 2026

Buka di editorUnduh .md

Cara menggunakan: Open it as soon as an AI incident or hazard is declared and update it as facts arrive. Fill the summary, harm and notification decisions first, because legal deadlines start at awareness. The main pitfall is waiting for the root cause before deciding whom to notify.

Pratinjau

AI Incident Report: [Short incident title]

Report for an event in which the development, use or malfunction of an AI system caused harm (an AI incident) or could plausibly have caused harm (an AI hazard or near miss). Concepts follow the OECD paper "Defining AI incidents and related terms" (2024); the notification section lists the serious incident reporting duty in Article 73 of the EU AI Act (Regulation (EU) 2024/1689) for providers of high-risk AI systems. Replace every [bracketed] value and every row marked Example. This is a documentation format, not legal advice.

Field	Value
Incident ID	[AI-INC-2026-001]
Classification	[AI incident / AI hazard (near miss)]
Severity	[SEV1 / SEV2 / SEV3 / SEV4]
Status	[Open / Contained / Resolved / Closed]
Incident lead	[@incident-lead]
Report author	[@author]
Date detected	YYYY-MM-DD HH:MM [timezone]
Last updated	YYYY-MM-DD
Confidentiality	[Confidential / Legal privilege, if advised]
Related	[Model card], [AI risk assessment], [Postmortem], [Ticket link]

1. Incident summary

[Summary.] Example: "Between 2 and 4 October the loan pre-screening model rejected applicants from one postcode area at four times the usual rate after an address-normalisation change upstream. About 1,150 applicants received automated rejections. The model was switched to manual review on 4 October at 16:20 and affected applications are being re-assessed."

2. System and version

Field	Value
AI system	[Name and purpose]
Model and version	[Model ID, version, training date]
Prompt or configuration version	[Version or commit]
Data pipeline version	[Version]
Deployment environment	[Production region, channel]
Our role	[Provider / Deployer / Both]
Risk classification	[Internal tier; EU AI Act category if assessed]

3. Timeline

Time (UTC)	Event	Source
Example: 2026-10-01 22:10	Upstream address normaliser v2.3 deployed	Change log CHG-8812
Example: 2026-10-02 08:00	Rejection rate for postcode area rises from 11% to 44%	Monitoring dashboard
Example: 2026-10-04 09:30	Complaint from broker escalated to model owner	Ticket SUP-5531
Example: 2026-10-04 11:05	Incident declared SEV2	Incident channel
Example: 2026-10-04 16:20	Model routed to manual review (containment)	Feature flag
[time]	[event]	[source]

4. Harm and affected parties

Harm type	Actual or potential	Affected parties	Number	Description
Example: Rights and legal obligations (non-discrimination)	Actual	Loan applicants in postcode area	1,150	Wrongful automated rejection
Example: Economic harm	Potential	Same applicants	Up to 1,150	Delayed access to credit
Example: Reputational harm	Actual	The organisation	n/a	Broker complaints, press enquiry
[Harm type]	[Actual / Potential]	[Parties]	[n]	[Description]

Vulnerable groups affected: [yes or no, with description].

5. Severity

Level	Definition used in this organisation
SEV1	Death or serious harm to health, serious disruption of critical infrastructure, large-scale rights infringement, or likely regulatory reportable incident
SEV2	Harm to many people or material financial loss; contained within days
SEV3	Limited harm to few people; quick containment
SEV4	Near miss with no harm; control weakness found

Assigned severity: [SEV2], because [reasoning].

6. Detection

Detected by: [monitoring alert / user complaint / staff / audit / external report].
Time from start to detection: [n] hours.
Why not earlier: Example: "Drift alerts are computed on the whole population; a shift in one area did not cross the global threshold."

7. Root cause analysis

Cause area	Finding	Contributing or root
Data	Example: Normaliser change mapped 6% of addresses to an unknown postcode, a feature the model associates with high risk	Root
Model	Example: Model relies strongly on postcode; no monotonic or fairness constraint	Contributing
Deployment	Example: Upstream change not covered by model owner's change review	Contributing
Human oversight	Example: Automated rejections below score 0.3 not reviewed	Contributing
Misuse or attack	Example: None found	n/a

The causal chain from change to harm is shown below.

8. Containment and remediation

ID	Action	Type	Owner	Status	Date
Example: C-01	Route all decisions to manual review	Containment	[@ops-lead]	Done	YYYY-MM-DD
Example: R-01	Roll back normaliser to v2.2	Remediation	[@data-eng]	Done	YYYY-MM-DD
Example: R-02	Re-assess 1,150 rejected applications manually	Redress	[@credit-ops]	In progress	YYYY-MM-DD
[ID]	[Action]	[Type]	[@owner]	[Status]	YYYY-MM-DD

9. Notifications

Recipient	Basis	Deadline	Sent	By	Reference
Executive and risk committee	Internal policy	[4 h for SEV1/SEV2]	YYYY-MM-DD	[@incident-lead]	[ref]
Market surveillance authority	EU AI Act Article 73, serious incident involving a high-risk AI system (provider; deployer where applicable)	Immediately once a causal link is established or reasonably likely; at the latest 15 days after awareness, 10 days in case of death, 2 days for a widespread infringement or serious critical infrastructure disruption (check the current text)	[n/a or date]	[@legal]	[ref]
Data protection authority	[GDPR Article 33, if a personal data breach]	[72 h]	[n/a or date]	[@dpo]	[ref]
Sector regulator	[Rule]	[Deadline]	[n/a or date]	[@compliance]	[ref]
Affected individuals	[Law, contract or policy]	[Deadline]	YYYY-MM-DD	[@comms]	[ref]
Provider or deployer counterpart	[Contract; AI Act cooperation duties]	[Deadline]	YYYY-MM-DD	[@vendor-manager]	[ref]

Decision not to notify: [reasoning, approved by @name on YYYY-MM-DD].

10. Lessons learned

Went well: [for example, the feature flag allowed containment in under 10 minutes once decided].
Did not go well: [for example, two days passed before a complaint reached the model owner].
Where we were lucky: [for example, the drift affected one area only].

11. Corrective and preventive actions

ID	Action	Owner	Due	Verification
Example: CAPA-01	Add segment-level drift alerts by region	[@ml-ops]	YYYY-MM-DD	Alert fires in replay of this incident
Example: CAPA-02	Add upstream feature producers to model change review	[@ml-governance]	YYYY-MM-DD	Change policy updated and trained
Example: CAPA-03	Human review of all automated rejections near threshold	[@credit-ops]	YYYY-MM-DD	Sample audit

12. Closure

Role	Name	Decision	Date
Incident lead	[@name]	[Close / Keep open]	YYYY-MM-DD
System owner	[@name]	[Decision]	YYYY-MM-DD
Risk or compliance	[@name]	[Decision]	YYYY-MM-DD

Standard and licence note

Based on the AI incident and AI hazard concepts in the OECD paper "Defining AI incidents and related terms" (OECD Artificial Intelligence Papers, 2024), used as general concepts only, and on Article 73 of Regulation (EU) 2024/1689 (EU legislative text, free to reuse). The text is original wording; consult the current consolidated Regulation and applicable guidance for binding deadlines. This is a documentation format, not legal advice. Template licence: MIT.

Templat terkait

AI & Data