AIGO — AI Incident Example

AIGO — AI Governance Operating Framework

Version: 0.1 Status: Draft Working Name: AIGO Full Name: AI Governance Operating Framework Document Identifier: AIGO-EXAMPLE-007 Document Type: Implementation Example Example Type: AI Incident Management

1. Purpose

This document provides an illustrative example of how an organization can identify, report, assess, contain, investigate, resolve, and learn from an AI-related incident using the AIGO AI Governance Operating Framework. The example demonstrates how an AI incident can be connected to:

the affected AI system;
lifecycle stage;
risk;
control;
evidence;
accountable roles;
incident severity;
containment;
investigation;
corrective action;
residual risk;
assurance;
continual improvement.

This document is an implementation example and does not constitute legal, regulatory, audit, certification, or legal-compliance advice.

2. Example Organization

For this example, the organization is ExampleCorp, a fictional organization implementing AIGO. The organization operates an AI-enabled recruitment-support system.

3. AI System

System Name: Candidate Assessment Assistant AI System ID: AI-HR-001 Business Function: Human Resources Classification: Class 3 — Enhanced Governance Lifecycle Stage: Operate System Owner: HR AI System Owner Model Owner: AI/ML Engineering Lead Risk Owner: Enterprise Risk Manager

4. Incident Scenario

During normal operation, ExampleCorp’s monitoring process detects an unexpected increase in differences between AI-generated candidate recommendations across demographic groups. The monitoring threshold is exceeded for two consecutive monitoring periods. The issue may indicate a potential fairness-related AI incident. The organization therefore initiates the AIGO AI Incident Management process.

5. Incident Objective

The objective is to ensure that the organization:

recognizes the incident;
records it;
assesses severity;
protects affected stakeholders;
contains the issue;
investigates root causes;
evaluates risk;
determines required notifications;
implements corrective action;
verifies effectiveness;
updates governance records;
captures lessons learned.

6. Incident Management Principle

An AI incident should be treated as a governance event, not merely as a technical fault. An incident may affect:

individuals;
business processes;
AI system performance;
risk exposure;
control effectiveness;
regulatory obligations;
organizational trust;
governance decisions.

7. Incident Lifecycle

Detect
   ↓
Report
   ↓
Register
   ↓
Triage
   ↓
Classify
   ↓
Contain
   ↓
Investigate
   ↓
Assess Impact
   ↓
Treat
   ↓
Recover
   ↓
Verify
   ↓
Close
   ↓
Learn
   ↓
Improve

8. Incident Detection

The monitoring system generates an alert. Alert ID: ALERT-2026-017 Indicator: Candidate recommendation disparity Threshold: Exceeded Detection Source: AI monitoring dashboard Detection Date: TBD Initial Status: Open

9. Initial Incident Record

Incident ID: INC-AI-001 AI System: AI-HR-001 Incident Type: Fairness / AI performance Detection Method: Automated monitoring Initial Severity: High Status: Under Investigation

10. Incident Description

The Candidate Assessment Assistant generated recommendation patterns that showed a statistically significant difference between monitored candidate groups. At the time of detection, the organization could not immediately determine whether the difference resulted from:

data quality;
model drift;
changes in candidate population;
feature distribution;
model behavior;
configuration changes;
upstream data changes.

11. Immediate Actions

The incident manager initiates:

incident registration;
system-owner notification;
risk-owner notification;
model-owner notification;
preservation of relevant evidence;
enhanced monitoring;
preliminary impact assessment.

12. Incident Triage

The incident is assessed against:

affected population;
duration;
severity;
likelihood of harm;
reversibility;
number of affected decisions;
potential discrimination;
potential legal or regulatory impact;
control failure;
recurrence potential.

13. Initial Severity

The incident is initially rated: Severity: High Reason:

the system supports employment-related processes;
affected individuals may experience adverse consequences;
the issue may involve fairness;
the scope is not yet fully understood.

14. Incident Severity Model

Severity	Description
Critical	Significant or potentially severe impact requiring immediate executive escalation
High	Material impact or significant governance concern
Medium	Moderate impact requiring formal management
Low	Limited impact with controlled consequences
Informational	Event requiring recording but no material impact

15. Incident Escalation

Incident Detected
      ↓
Initial Triage
      ↓
Severity Assessment
      ↓
Low ─────────→ Normal Management
      ↓
Medium ──────→ Formal Management
      ↓
High ────────→ Governance Escalation
      ↓
Critical ────→ Executive / Emergency Escalation

16. Containment Objective

The immediate objective is to prevent additional potentially affected recruitment decisions while preserving evidence required for investigation.

17. Containment Decision

ExampleCorp decides to:

suspend automated candidate-ranking recommendations;
preserve the underlying model version;
preserve relevant input and output records;
require manual assessment by authorized recruitment personnel;
increase monitoring frequency;
initiate investigation.

The underlying recruitment process remains operational under enhanced human review.

18. Containment Status

Automated recommendation function: Suspended Human recruitment process: Active AI system: Restricted operation Incident: High severity / Open

19. Evidence Preservation

The following evidence is preserved:

model version;
model configuration;
relevant input data;
recommendation outputs;
monitoring results;
system logs;
access logs;
change records;
deployment records;
risk assessments;
control assessments;
previous fairness assessments;
incident communications.

20. Evidence Chain

Incident
   ↓
AI System
   ↓
Model Version
   ↓
Input Data
   ↓
AI Output
   ↓
Monitoring Alert
   ↓
Control
   ↓
Risk
   ↓
Decision

21. Investigation Team

ExampleCorp establishes an investigation team consisting of:

AI Incident Manager;
Model Owner;
Data Owner;
AI Governance Lead;
Risk Manager;
HR Process Owner;
Privacy representative where applicable;
Security representative where applicable;
Assurance representative where appropriate.

22. Investigation Independence

Where practical, investigation responsibilities should include sufficient independence from the individuals directly responsible for the system’s operation. The objective is to reduce conflicts of interest and improve reliability of findings.

23. Investigation Questions

The investigation asks:

What changed?
When did it change?
Which model version was active?
Which data was used?
Which candidates were affected?
How many outputs were affected?
Did the system change?
Did upstream data change?
Were controls operating?
Were monitoring thresholds appropriate?
Was there an unauthorized change?
Was the issue previously detectable?
Did affected decisions rely on the AI output?
What harm may have occurred?

24. Timeline

Time	Event
T-30 days	Previous fairness monitoring completed
T-14 days	Data pipeline updated
T-7 days	Model monitoring continued normally
T0	Alert threshold exceeded
T+1 hour	Incident registered
T+3 hours	AI recommendation function restricted
T+1 day	Investigation begins
T+3 days	Root cause identified
T+5 days	Corrective action implemented
T+7 days	Retesting completed
T+10 days	Governance review
T+14 days	Incident closure

25. Root Cause Investigation

The investigation identifies that an upstream data transformation introduced a distribution change in one feature used by the model. The change was technically authorized as a data-pipeline maintenance activity but was not assessed as an AI-impacting change.

26. Root Cause

Primary Root Cause: The change-management process did not adequately identify the AI impact of an upstream data transformation. Contributing Factors:

incomplete change-impact assessment;
insufficient AI-specific change classification;
monitoring threshold delay;
inadequate linkage between data changes and AI governance.

27. Control Failure

The incident reveals a weakness in: Control: AIGO-C-013 — AI Change Management The control existed but did not sufficiently capture upstream data changes affecting AI behavior. Control Rating: Partially Effective.

28. Risk Relationship

The incident is linked to:

Risk ID	Risk
R-001	Biased recommendations
R-005	Inaccurate recommendations
R-006	Model/data drift
R-007	Uncontrolled change

29. Impact Assessment

The investigation determines:

1,250 candidate records were processed during the affected period;
180 candidates received recommendations potentially influenced by the affected feature;
32 candidates progressed based partly on AI recommendations;
final recruitment decisions remained subject to human review.

The organization therefore determines that the incident had a material but contained impact.

30. Affected Stakeholders

Potentially affected stakeholders include:

candidates;
recruitment personnel;
HR management;
AI system owner;
business leadership;
risk management;
governance committee.

31. Human Decision Review

ExampleCorp manually reviews the affected recruitment cases. The review determines whether:

AI output materially influenced the decision;
human reviewers challenged the output;
decisions remain appropriate;
corrective action is necessary.

Where necessary, cases are reassessed.

32. Harm Assessment

The organization evaluates:

actual harm;
potential harm;
discrimination risk;
privacy impact;
financial impact;
reputational impact;
operational impact.

The investigation concludes that no irreversible decision was made solely by the AI system.

33. Notification Assessment

The organization assesses whether notification is required under applicable:

organizational policies;
contractual obligations;
regulatory requirements;
privacy requirements;
incident-management requirements.

Notification decisions must be made by appropriately authorized personnel.

34. Corrective Action

ExampleCorp implements:

correction of the upstream data transformation;
expanded change-impact assessment;
AI-specific change classification;
new data-drift monitoring;
tighter fairness thresholds;
mandatory governance review for material upstream changes.

35. Corrective Action Register

Action ID	Action	Owner	Priority
CA-INC-001	Correct data transformation	Data Owner	Critical
CA-INC-002	Update change assessment	Change Manager	High
CA-INC-003	Add data-drift monitoring	Model Owner	High
CA-INC-004	Review fairness thresholds	Model Owner	High
CA-INC-005	Update governance procedure	AI Governance Lead	Medium

36. Corrective Action Lifecycle

Incident Finding
      ↓
Root Cause
      ↓
Corrective Action
      ↓
Implementation
      ↓
Evidence
      ↓
Retest
      ↓
Effectiveness Assessment
      ↓
Closure

37. Technical Remediation

The data transformation is corrected. The affected model inputs are recalculated. The model is evaluated using:

corrected data;
historical comparison data;
fairness test data;
performance test data.

38. Control Remediation

The change-management control is updated so that changes to:

training data;
inference data;
data pipelines;
feature transformations;
model configuration;
model dependencies

must be evaluated for potential AI impact.

39. Monitoring Improvements

ExampleCorp introduces:

data-distribution monitoring;
feature-drift detection;
subgroup performance monitoring;
fairness alerts;
automated escalation;
change-to-monitoring linkage.

40. Enhanced Monitoring

For the first 30 days following remediation, monitoring frequency is increased. Example:

Indicator	Normal	Enhanced
Fairness	Weekly	Daily
Data drift	Daily	Continuous
Model performance	Daily	Continuous
Incident alerts	Standard	Immediate

41. Retesting

The organization conducts a retest after remediation. The retest evaluates:

corrected data;
model outputs;
fairness indicators;
control operation;
monitoring alerts;
change-management records.

42. Retest Result

The retest demonstrates:

data transformation corrected;
fairness indicators returned within approved thresholds;
monitoring detects similar changes;
change process identifies AI-impacting data changes;
required evidence is generated.

Control Status: Effective after remediation.

43. Residual Risk

Following remediation:

Risk	Before	After
Biased recommendations	High	Medium
Inaccurate recommendations	High	Medium
Data/model drift	High	Low
Uncontrolled change	High	Medium

Residual risks remain subject to ongoing monitoring.

44. Incident Recovery

The AI recommendation function may resume operation only after:

technical remediation;
control remediation;
retesting;
risk reassessment;
governance review;
documented authorization.

45. Recovery Decision

Decision: Resume Restricted Operation Conditions:

enhanced monitoring for 30 days;
weekly governance reporting;
completed affected-case review;
verified change-control improvements.

46. Incident Closure Criteria

The incident may be closed when:

containment is complete;
root cause is documented;
impact assessment is complete;
corrective actions are implemented;
required notifications are addressed;
controls are retested;
residual risk is acceptable;
management approves closure;
lessons learned are recorded.

47. Incident Closure Decision

Incident ID: INC-AI-001 Status: Closed Closure Basis:

root cause identified;
impact assessed;
affected cases reviewed;
remediation implemented;
control retested;
residual risk accepted;
improvement actions established.

48. Lessons Learned

The incident demonstrates that:

upstream data changes can become AI governance events;
change management must include AI impact assessment;
monitoring should cover data as well as model outputs;
human oversight can reduce potential harm;
incident management must connect technical and governance processes;
control effectiveness should be reassessed after incidents.

49. Governance Improvements

Following the incident, ExampleCorp updates:

AI change-management procedure;
AI monitoring procedure;
control assessment methodology;
risk assessment methodology;
data governance requirements;
incident-management procedure;
approval criteria.

50. Incident-to-Improvement Relationship

Incident
   ↓
Investigation
   ↓
Root Cause
   ↓
Control Weakness
   ↓
Corrective Action
   ↓
Retest
   ↓
Lessons Learned
   ↓
Framework Improvement

51. Incident and Lifecycle

The incident occurred during the Operate stage but affected multiple lifecycle elements.

Operate
   ↓
Monitor
   ↓
Detect Incident
   ↓
Assess
   ↓
Contain
   ↓
Investigate
   ↓
Treat
   ↓
Recover
   ↓
Monitor
   ↓
Improve

52. Incident and Risk Management

An incident can change the organization’s understanding of risk. The organization therefore updates:

affected risk;
likelihood;
impact;
control effectiveness;
residual risk;
treatment strategy.

53. Incident and Control Assessment

The incident revealed that AIGO-C-013 was insufficiently designed to identify AI-impacting upstream data changes. The control is therefore reassessed. Previous Rating: Partially Effective Post-remediation Rating: Effective

54. Incident Evidence

Evidence retained includes:

incident record;
monitoring alert;
logs;
model version;
data transformation record;
investigation report;
affected-case analysis;
corrective-action records;
retest results;
approval to resume operation;
lessons-learned record.

55. Incident Evidence Chain

Detection
   ↓
Incident Record
   ↓
Investigation Evidence
   ↓
Root Cause
   ↓
Corrective Action
   ↓
Retest Evidence
   ↓
Closure Decision

56. Incident Traceability

The incident is traceable to:

Element	Identifier
AI System	AI-HR-001
Incident	INC-AI-001
Alert	ALERT-2026-017
Risk	R-001
Control	AIGO-C-013
Finding	INC-F-001
Corrective Action	CA-INC-001
Retest	RT-INC-001
Closure	CLS-INC-001

57. Incident Record

Field	Value
Incident ID	INC-AI-001
System	AI-HR-001
Type	Fairness / Data / Change
Severity	High
Detection	Monitoring
Status	Closed
Root Cause	Unassessed AI impact of data change
Affected Records	1,250
Potentially Affected	180
Corrective Actions	5
Control Reassessment	Completed
Residual Risk	Medium
Closure Authority	AI Governance Committee

58. Incident Communication

Incident communications should be:

factual;
timely;
authorized;
appropriately scoped;
documented.

The organization should avoid speculation until sufficient facts have been established.

59. Incident Escalation Criteria

Escalation should occur when an incident involves:

potential significant harm;
discrimination;
privacy impact;
security compromise;
unauthorized operation;
critical control failure;
widespread impact;
significant regulatory concern;
material reputational risk.

60. Incident Management Roles

Role	Responsibility
Incident Manager	Coordinates incident response
System Owner	Provides system accountability
Model Owner	Technical investigation
Risk Owner	Risk assessment
Data Owner	Data investigation
Governance Lead	Governance escalation
Security	Security assessment
Privacy	Privacy assessment
Assurance	Independent review where required
Management	Decision and risk acceptance

61. Incident Decision Authority

The incident manager coordinates the response but does not necessarily have authority to:

accept significant residual risk;
approve system resumption;
close high-severity incidents;
override governance requirements.

Those decisions remain with authorized governance roles.

62. Incident Management Checklist

63. Relationship to AIGO Procedures

This example should be implemented through the applicable AIGO procedures, particularly:

AI Incident Management Procedure;
AI Governance Procedure;
AI Risk Assessment Procedure;
AI Control Assessment Procedure;
AI Monitoring Procedure;
AI Change Management Procedure;
AI Assurance Procedure;
AI Risk Acceptance Procedure;
AI Approval Procedure;
Continuous Improvement Procedure.

64. Relationship to AIGO Controls

The incident demonstrates how controls interact across the lifecycle. Relevant controls include:

governance accountability;
risk management;
monitoring;
human oversight;
change management;
incident management;
assurance;
continual improvement.

65. Relationship to ISO/IEC 42001

AI incident management can provide evidence relevant to an AI management system’s processes for:

risk management;
operational control;
monitoring;
incident handling;
corrective action;
continual improvement.

Applicable ISO/IEC 42001 requirements should be evaluated separately by the implementing organization.

66. Relationship to NIST AI RMF

The incident process can support activities associated with:

Function	Incident Contribution
GOVERN	Accountability and escalation
MAP	Context and impact analysis
MEASURE	Monitoring and investigation
MANAGE	Response, treatment and improvement

67. Example Incident Management Model

AI System
   ↓
Risk
   ↓
Control
   ↓
Monitoring
   ↓
Incident
   ↓
Containment
   ↓
Investigation
   ↓
Corrective Action
   ↓
Retest
   ↓
Residual Risk
   ↓
Approval to Resume
   ↓
Continual Improvement

68. Key Lessons

68.1 Detection Must Lead to Action

An alert is not sufficient. It must initiate an appropriate governance response.

68.2 Incidents Should Be Evidence-Based

The investigation should preserve reliable evidence.

68.3 Containment Protects Stakeholders

Temporary restriction may be appropriate while facts are established.

68.4 Root Cause Matters

Corrective action should address why the incident occurred.

68.5 Controls Must Be Reassessed

An incident may demonstrate that an existing control is inadequately designed or operated.

68.6 Closure Requires Verification

The organization should demonstrate that corrective action worked before closing a material incident.

68.7 Incidents Should Improve the Framework

Lessons learned should feed risk management, procedures, controls, monitoring, and governance.

69. Complete AI Incident Model

Detect
   ↓
Report
   ↓
Register
   ↓
Triage
   ↓
Classify
   ↓
Contain
   ↓
Investigate
   ↓
Assess Impact
   ↓
Assess Risk
   ↓
Correct
   ↓
Retest
   ↓
Recover
   ↓
Monitor
   ↓
Close
   ↓
Learn
   ↓
Improve

70. Document Status

Document: AIGO — AI Incident Example Version: 0.1 Status: Draft Working Name: AIGO Full Name: AI Governance Operating Framework Document Identifier: AIGO-EXAMPLE-007 Document Type: Implementation Example Example Type: AI Incident Management This document provides an illustrative example of how an AI-related incident can be detected, assessed, contained, investigated, remediated, retested, closed, and used for continual improvement within the AIGO AI Governance Operating Framework.

71. End of Example Document

AIGO — AI Incident Example Document ID: AIGO-EXAMPLE-007 Version: 0.1 Status: Draft End of Document

​AIGO — AI Incident Example

​AIGO — AI Governance Operating Framework

​1. Purpose

​2. Example Organization

​3. AI System

​4. Incident Scenario

​5. Incident Objective

​6. Incident Management Principle

​7. Incident Lifecycle

​8. Incident Detection

​9. Initial Incident Record

​10. Incident Description

​11. Immediate Actions

​12. Incident Triage

​13. Initial Severity

​14. Incident Severity Model

​15. Incident Escalation

​16. Containment Objective

​17. Containment Decision

​18. Containment Status

​19. Evidence Preservation

​20. Evidence Chain

​21. Investigation Team

​22. Investigation Independence

​23. Investigation Questions

​24. Timeline

​25. Root Cause Investigation

​26. Root Cause

​27. Control Failure

​28. Risk Relationship

​29. Impact Assessment

​30. Affected Stakeholders

​31. Human Decision Review

​32. Harm Assessment

​33. Notification Assessment

​34. Corrective Action

​35. Corrective Action Register

​36. Corrective Action Lifecycle

​37. Technical Remediation

​38. Control Remediation

​39. Monitoring Improvements

​40. Enhanced Monitoring

​41. Retesting

​42. Retest Result

​43. Residual Risk

​44. Incident Recovery

​45. Recovery Decision

​46. Incident Closure Criteria

​47. Incident Closure Decision

​48. Lessons Learned

​49. Governance Improvements

​50. Incident-to-Improvement Relationship

​51. Incident and Lifecycle

​52. Incident and Risk Management

​53. Incident and Control Assessment

​54. Incident Evidence

​55. Incident Evidence Chain

​56. Incident Traceability

​57. Incident Record

​58. Incident Communication

​59. Incident Escalation Criteria

​60. Incident Management Roles

​61. Incident Decision Authority

​62. Incident Management Checklist

​63. Relationship to AIGO Procedures

​64. Relationship to AIGO Controls

​65. Relationship to ISO/IEC 42001

​66. Relationship to NIST AI RMF

​67. Example Incident Management Model

​68. Key Lessons

​68.1 Detection Must Lead to Action

​68.2 Incidents Should Be Evidence-Based

​68.3 Containment Protects Stakeholders

​68.4 Root Cause Matters

​68.5 Controls Must Be Reassessed

​68.6 Closure Requires Verification

​68.7 Incidents Should Improve the Framework

​69. Complete AI Incident Model

​70. Document Status

​71. End of Example Document