Lilly Tech Systems
AI Incident Response
Prepare for and respond to AI-specific security incidents. Learn detection techniques, triage frameworks, containment strategies, model rollback procedures, and how to build comprehensive incident response playbooks.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
What makes AI incidents unique? Understanding the AI incident landscape and why traditional IR falls short.
2. Detection
Model failure detection, anomaly monitoring, drift detection, and automated alerting for AI systems.
3. Triage
Severity classification, impact assessment, AI-specific incident categorization, and escalation procedures.
4. Containment
Model isolation, rollback strategies, traffic rerouting, feature flags, and limiting blast radius.
5. Recovery
Model retraining, data remediation, system restoration, post-incident analysis, and lessons learned.
6. Best Practices
IR playbooks for AI, tabletop exercises, team structures, communication plans, and continuous improvement.
What You'll Learn
By the end of this course, you'll be able to:
Detect AI Incidents
Set up monitoring and alerting systems that catch model failures, adversarial attacks, and data drift in real time.
Triage Effectively
Classify AI incidents by severity and impact using frameworks designed for machine learning systems.
Contain and Recover
Execute model rollbacks, isolate compromised systems, and restore normal operations with minimal downtime.
Build IR Playbooks
Create comprehensive incident response playbooks tailored to AI-specific failure modes and attack scenarios.