Incident Management (AI) is the mandatory, systematic process required by the EU AI Act for providers of high-risk AI systems to detect, investigate, mitigate, and report any malfunction, performance deviation, or serious incident that occurs during the system’s operational lifecycle, ensuring a swift response to protect health, safety, and fundamental rights.

This process extends beyond the immediate reporting of serious events to authorities; it represents a comprehensive, closed-loop governance system embedded within the provider's Quality Management System (QMS). Its purpose is to manage all adverse events, from minor malfunctions to catastrophic failures, with rigor and transparency, transforming incidents into critical learning opportunities to prevent recurrence. Effective incident management is the operational backbone of the Act’s accountability principle, requiring providers to not only react to problems but to proactively analyze root causes and implement systemic corrections that strengthen the overall safety and compliance of their AI systems.

A robust AI incident management framework under the EU AI Act typically follows a structured lifecycle:

Detection & Triage: Implementing monitoring channels (e.g., user reports, automated alerts from post-market monitoring) to identify potential incidents and triage them based on severity, with immediate containment actions for high-risk events.

Investigation & Root Cause Analysis: Conducting a thorough, documented investigation to determine the technical, procedural, or contextual cause of the incident, distinguishing between model errors, data drift, adversarial attacks, or misuse.

Corrective & Preventive Actions (CAPA): Implementing immediate corrective actions to address the specific incident (e.g., halting the system, issuing a patch) and deriving preventive actions to address underlying weaknesses in the design, development process, or QMS to prevent similar future incidents.

Reporting & Communication: Fulfilling legal obligations by reporting serious incidents to national market surveillance authorities within 15 days, providing transparent updates to deployers, and, where necessary, communicating with affected individuals or the public.

Record-Keeping & Learning: Meticulously documenting every stage of the incident, the analysis, and actions taken, and feeding these findings back into the risk management file, training protocols, and design improvements for future system versions.

Regulatory Context: The obligation for incident management is primarily established under Article 62 of the EU AI Act, which mandates the reporting of serious incidents. This obligation is supported by the broader requirements for a QMS (Article 17), post-market monitoring (Article 61), and the provider’s duty to take corrective actions (Article 21). The process aligns with established international standards like ISO/IEC 42001, which includes clauses for incident preparation and response within an AI Management System (AIMS).

Resilience and Trust Imperative: A disciplined incident management process is a critical component of organizational resilience and regulatory trust. It minimizes harm, demonstrates responsible stewardship to authorities and customers, and provides a defensible audit trail in the event of legal scrutiny. Conversely, poor incident handling can compound initial harm, lead to escalated regulatory penalties, and irreparably damage an organization’s reputation and license to operate in the AI sector.