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Incident Management & 72h Reporting

Meet the EU AI Act's 72-hour serious incident reporting deadline with automated workflows.

EU AI Act Article 62 requires providers of high-risk AI systems to report serious incidents to national authorities within 72 hours. BaFin and DORA add further incident reporting obligations for financial institutions (24-hour initial notification, 72-hour detailed report). Our incident management module provides a structured lifecycle — from initial report through investigation and root cause analysis to authority notification — with built-in deadline tracking, 5-whys methodology, and full audit trails.

What We Deliver

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Structured Incident Reporting

Severity-classified incident submission covering EU AI Act categories: death, serious health harm, property damage, environmental impact, socioeconomic harm, and fundamental rights violations.

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72-Hour Deadline Tracking

Automated countdown timers from incident confirmation, with escalation alerts at 24h, 48h, and 72h milestones ensuring you never miss a regulatory reporting deadline.

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Root Cause Analysis (5-Whys)

Structured investigation framework using the 5-whys methodology with contributing factor analysis, evidence attachment, and remediation step tracking to prevent recurrence.

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Authority Notification Workflow

End-to-end workflow from REPORTED → INVESTIGATING → CONFIRMED → REPORTED TO AUTHORITY → RESOLVED with full audit trail, MTTR analytics, and reporting compliance rates.

Key Outcomes

  • check_circle EU AI Act Article 62 serious incident reporting compliance
  • check_circle DORA 24h/72h incident notification requirement coverage
  • check_circle Structured root cause analysis with evidence documentation
  • check_circle Complete audit trail and MTTR analytics for regulatory review

Ready to Get Started?

Let us help you build AI systems that are ethical, compliant, and trustworthy. Schedule a consultation to discuss your needs.

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Responsible AI governance platform for European enterprises, SMEs, and startups. EU AI Act compliance, bias detection, and model explainability.

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