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A set of processes for detecting, triaging, resolving, and learning from production service incidents to restore normal operations quickly.
DESCRIPTION
Incident management provides structured processes for handling outages and degradations. It includes detection, alerting, triage, mitigation, communication, and post-incident learning. Clear severity definitions and roles streamline response. Runbooks and automation reduce time to restore service. Blameless postmortems drive systemic improvements. Effective incident management enhances reliability, customer trust, and regulatory compliance.
ALTERNATIVE DEFINITIONS
Atlassian
Incident management is the process used by DevOps and IT operations teams to respond to unplanned events or service interruptions and restore service operation as quickly as possible.
Emphasizes response speed and operational restoration by cross-functional teams.
NIST SP 800-61
Incident handling includes preparation, detection and analysis, containment, eradication, and recovery.
Outlines the canonical phases of incident response lifecycle.
AWS Well-Architected
Prepare and run incident response to restore workloads to a known good state with minimal business impact.
Focuses on preparedness and minimizing business impact in cloud workloads.
RELATED TERMS
Incident Response On-call Postmortem Root Cause Analysis Change Management Runbook SLA Service Level Objective Monitoring
COMPETING TERMS
KNOWLEDGE AREAS
Observability & Telemetry Software Engineering Platform & Tooling Data Engineering MLOps
TARGET AUDIENCE
DevOps Engineer MLOps Engineer Software Developer Cloud Architect Data Engineer Chief Technology Officer Chief Information Officer
EXTERNAL SOURCES
- Wikipedia - Incident management
- Google SRE Book - Best practices for incident response
- NIST - Computer security incident handling guide
- PagerDuty - Operational incident response practices
TOP QUESTIONS
What are the phases of Incident Management?
Detection, triage, mitigation, communication, resolution, and post-incident review are core phases, supported by preparation and tooling.
Category: Overview
What are common mistakes during incidents?
Unclear ownership, noisy alerts, Ad-hoc communication, and skipping postmortems lead to repeated failures and longer outages.
Category: Mistakes
How is severity defined in Incident Management?
Severity levels reflect business impact and urgency; define clear criteria for customer effect, data loss, and scope.
Category: Requirements
Which metrics show Incident Management effectiveness?
MTTD, MTTR, incident volume, pages per on-call, and action item completion rate indicate maturity.
Category: Measurement
What is a blameless postmortem?
A structured review focusing on learning and systemic fixes rather than individual blame, improving future resilience.
Category: Example
How should communication work during incidents?
Designate an incident commander and communications lead; provide frequent, accurate updates to stakeholders and customers.
Category: Best practices
What tools support Incident Management?
Alerting, paging, chat-ops, ticketing, runbooks, and observability platforms coordinate detection and response.
Category: Overview
How does Incident Management apply to ML systems?
Include model-specific playbooks for drift, data outages, and degraded accuracy, with SLIs for freshness and prediction quality.
Category: Example
How to reduce burnout in Incident Management?
Implement sustainable on-call rotations, auto-remediation, noise reduction, and fair compensation with follow-up improvements.
Category: Best practices