When AI systems fail, respond with confidence
Structured incident tracking from detection through resolution—with the audit trail regulators expect.
The challenge
AI incidents are different—and regulators are watching
When an AI system malfunctions, causes harm, or behaves unexpectedly, organizations need to respond quickly and document thoroughly. Unlike traditional IT incidents, AI incidents may require regulatory reporting, can affect fundamental rights, and demand specific harm categorization. Ad-hoc incident tracking leaves organizations exposed.
No standardized way to classify AI-specific incident types and severity
Harm categories required by regulations aren't captured in generic ticketing systems
Investigation progress and corrective actions aren't tracked systematically
Regulatory reporting deadlines can be missed without proper workflows
Incident history needed for audits is scattered or incomplete
Benefits
Why use Incident management?
Key advantages for your AI governance program
Capture incidents with structured severity and harm classification
Move incidents through a clear lifecycle with accountability
Document corrective actions and lessons learned
Meet regulatory reporting requirements with complete records
Capabilities
What you can do
Core functionality of Incident management
Structured incident capture
Log incidents with severity levels, harm categories, affected systems, and dates—everything needed for regulatory reporting.
Lifecycle management
Track incidents through Open → Investigating → Mitigated → Closed with clear ownership and status visibility.
Impact documentation
Record harm categories, affected persons, causality analysis, and immediate mitigations for complete incident records.
Approval workflows
Route serious incidents through approval before regulatory submission, with full audit trail of who approved what and when.
How it works
See it in action
Explore the key functionality of Incident management
Incident tracking
Log and track AI-related incidents with severity, status, and resolution timeline
Incident timeline
View complete incident history with actions taken and lessons learned
Enterprise example
How an organization turned incident chaos into compliance confidence
See how organizations use this capability in practice
The challenge
An organization's AI-powered customer service system started providing incorrect information to users. The incident was reported via email, investigated through chat messages, and documented in a Word document. When the compliance team needed to prepare a regulatory report, they spent days piecing together what happened, who was involved, and what corrective actions were taken.
The solution
They implemented a structured incident management system where all AI incidents are logged with standardized severity levels, harm categories, and incident types. Each incident moves through a defined lifecycle with clear ownership, and serious incidents require approval before being marked as resolved.
The outcome
When the next incident occurred, the organization captured all required information at the point of logging. The investigation was tracked in one place, corrective actions were documented, and the approval workflow ensured proper review before closure. Regulatory reporting that previously took days now takes hours, with complete audit trails.
Why VerifyWise
Built for AI incident complexity
What makes our approach different
AI-specific incident taxonomy
Seven incident types tailored to AI systems: Malfunction, Unexpected Behavior, Model Drift, Misuse, Data Corruption, Security Breach, and Performance Degradation.
Regulatory-aligned harm categories
Five harm categories matching EU AI Act requirements: Health, Safety, Fundamental Rights, Property, and Environment. Capture exactly what regulators need.
Enforced lifecycle integrity
Incidents progress through Open → Investigating → Mitigated → Closed. Once closed, they cannot be reopened—preserving audit trail integrity.
Approval before submission
Serious incidents can require approval before regulatory reporting. Track who approved, when, and with what notes for complete accountability.
Regulatory context
Meeting incident reporting requirements
AI regulations require organizations to track, report, and learn from incidents. Structured incident management ensures you capture the right information and can demonstrate proper response procedures to regulators.
Providers of high-risk AI systems must report serious incidents to market surveillance authorities, including incidents that resulted in death, serious damage to health, property, or the environment.
Deployers must monitor AI systems and inform providers of serious incidents or malfunctions that could lead to risks to health, safety, or fundamental rights.
Organizations must establish processes for reporting and responding to AI-related incidents as part of their AI management system.
Technical details
How it works
Implementation details and technical capabilities
Automatic INC-ID generation for unique incident identification and tracking
4-stage lifecycle: Open→Investigating→Mitigated→Closed (closed incidents cannot be reopened)
EU AI Act Article 73 compliance for serious incident reporting with harm category tracking
7 incident types: Malfunction, Unexpected Behavior, Model Drift, Misuse, Data Corruption, Security Breach, Performance Degradation
5 harm categories aligned with EU AI Act: Health, Safety, Fundamental Rights, Property, Environment
Approval workflow with Pending, Approved, Rejected, and Not Required statuses
CE marking integration for linking incidents to conformity assessments
Supported frameworks
Integrations
FAQ
Common questions
Frequently asked questions about Incident management
More from Govern
Related capabilities
Other features in the Govern pillar
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See how VerifyWise can help you govern AI with confidence.