Incident Root Cause Categorization Checklist
Purpose: Systematically categorize root causes to prevent recurrence. Move from ad-hoc fixes to structured improvements.
Root Cause Categories
| Category | Definition | Common Examples |
|---|---|---|
| Infrastructure | Hardware/OS/network failure | Server crash, disk full, network partition |
| Human Error | Process, process, process | Wrong config, delete data, mis-delete |
| Development | Code defect, missing tests | Bug, unhandled exception, race condition |
| Operational | Change management failure | Deploy went bad, rollback failed |
| Third-Party | External dependency failure | Vendor outage, API limit reached |
| External | Outage not caused by you | Internet outage, cloud provider outage |
| Process | Gap in procedure, not technology | Missing SOP, unclear escalation |
| Change Request | Unplanned change caused issue | Feature introduced bug |
| Data Integrity | Corrupted data, stale state | Missing migration, orphaned records |
| Misconfiguration | Wrong settings applied | Wrong region, incorrect environment |
Root Cause Analysis Flow
1. Immediate Investigation
- ☐ Review alert context: What triggered the alert? Metric thresholds?
- ☐ Examine logs: System logs, application logs, error traces
- ☐ Check recent changes: Deployments (commit hash), config changes, environment variables
- ☐ Interview witnesses: Who saw what? When?
2. Hypothesis Generation
- ☐ Hypothesis 1: Infrastructure failure
- ☐ Hypothesis 2: Human error
- ☐ Hypothesis 3: Development defect
- ☐ Hypothesis 4: Operational failure
- ☐ Hypothesis 5: Third-party dependency
- ☐ Hypothesis 6: External factor
3. Evidence Collection
- ☐ Collect logs: Full logs around incident timeframe
- ☐ Capture metrics: CPU, memory, disk, network, error rates
- ☐ Review changes: Git commits, config diffs, deployment manifests
- ☐ Preserve evidence: Screenshot, database snapshot, network capture
- ☐ Interview team members: First responders, developers, operations
4. Verification
- ☐ Test hypothesis 1: Simulate or repeat failure
- ☐ Test hypothesis 2: Verify configuration/state
- ☐ Test hypothesis 3: Review code, reproduce bug
- ☐ Test hypothesis 4: Confirm change was applied
- ☐ Test hypothesis 5: Check external status pages
- ☐ Test hypothesis 6: Verify internet/cloud status
5. Final Categorization
- ☐ Confirm root cause: Which category matches best?
- ☐ Confirm contributing factors: Anything else?
- ☐ Confirm evidence: Logs? Metrics? Screenshots?
- ☐ Confirm confidence level: High / Medium / Low
RCA Documentation Fields
- ☐ Root Cause Category: _______________
- ☐ Root Cause Description: _______________
- ☐ Contributing Factors: _______________
- ☐ Evidence Collected:
- [ ] Logs collected - [ ] Metrics captured - [ ] Screenshots taken - [ ] Snapshots preserved
- ☐ First Responder Names: _______________
- ☐ Investigation Duration: ____ hours
- ☐ Confidence Level: High / Medium / Low
Prevention Checklist (by Category)
Infrastructure → Prevention
- ☐ Monitor hardware health: CPU, memory, disk, network
- ☐ Automated failover: Multi-AZ? Load balancer health checks?
- ☐ Capacity planning: Auto-scaling enabled? Scaling rules defined?
Human Error → Prevention
- ☐ Review approval gates: Config changes need review?
- ☐ Sanity checks: Can't delete critical data without confirmation?
- ☐ Training: Regular on-call training, incident drills
Development → Prevention
- ☐ Test coverage: Unit tests, integration tests, E2E tests
- ☐ Code review: All PRs reviewed?
- ☐ Pre-deployment checks: Staging environment validated?
Operational → Prevention
- ☐ Automated rollback: Immediate rollback on failure?
- ☐ Deployment safety: Canary deployments? Feature flags?
- ☐ Monitoring: Alerting before production impact?
Third-Party → Prevention
- ☐ Vendor SLAs: Documented response times and penalties?
- ☐ Alternative providers: What happens if this vendor fails?
- ☐ Health checks: Dependency uptime monitoring?
External → Prevention
- ☐ External dependency alerts: Notify when outage detected?
- ☐ Mitigation plans: Workarounds if dependency fails?
- ☐ Diversify dependencies: Multi-cloud, multi-provider?
Process → Prevention
- ☐ SOPs documented: All procedures written down?
- ☐ Rollout procedures: Training documented? Checked?
- ☐ Post-mortem process: Root cause captured and shared?
Change Request → Prevention
- ☐ Feature flags: Rollback without code change?
- ☐ Gradual rollout: 10% → 50% → 100% with monitoring?
- ☐ Observability: Can we see impact before 100% rollout?
Data Integrity → Prevention
- ☐ Data migration tests: Verified before production?
- ☐ Incremental backups: RPO target met?
- ☐ Data validation: Schema validation, data integrity checks?
Misconfiguration → Prevention
- ☐ Environment consistency: Config sync across environments?
- ☐ Secret management: Environment variables encrypted?
- ☐ Validation: Can't deploy invalid config?
Usage: Complete after post-mortem. Use to prevent recurrence of similar incidents. Validate against existing post-mortem template: incident-post-mortem-template.html.
Product links: /l/ops-starter-kit-vol-2 | /l/ops-starter-kit | /l/automation-starter-pack
From the HIVE80lab kit
- The First 30 Minutes — free incident quick-start
- Ops Starter Kit — full incident-response kit for small teams — $14
- Ops Mega Bundle — all 5 kits in one download — $29
- Small-Team Ops Audit — prioritized findings + fix plan, five-day turnaround — $149
- Custom Incident Runbook — done-for-you, built from your estate, 48h — $249
Part of the five-pillar incident-response set: see the pillars overview and the blameless post-incident review template.