AI in Monitoring & Incident Response
Measures how AI tools enable proactive issue detection, rapid root cause analysis, and informed response.
Sample assessment questions for each level:
- Level -1: “Is there an explicit policy against using AI for monitoring or incident response?”
- Level 0: “Do individual operators use AI monitoring tools without coordination?”
- Level 1: “Has the team identified monitoring and incident response areas for AI enhancement?”
- Level 2: “Is AI used for log aggregation and anomaly detection?”
- Level 3: “Are alerts triaged using AI severity/risk assessment?”
- Level 4: “Does AI correlate incidents across systems to detect root cause?”
- Level 5: “Does an intelligent assistant suggest remediations or trigger automated fixes?”
Key metrics to track:
- Mean time to detect (MTTD): Reduction in time to detect issues with AI monitoring
- Mean time to resolve (MTTR): Reduction in incident resolution time with AI assistance
- False positive rate: Percentage of AI alerts that aren’t actual issues
- Root cause identification accuracy: Percentage of incidents where AI correctly identifies the root cause
- Proactive resolution rate: Percentage of the potential problems resolved before user impact
