Catalog
concept#Governance#Reliability#Observability#Security

Operational Risk

Concept for identifying, assessing and managing non-financial risks arising from processes, systems, people or external events.

Operational risk covers losses from failed processes, systems, people, or external events.
Established
Medium

Classification

  • Medium
  • Organizational
  • Organizational
  • Intermediate

Technical context

Incident management systems (e.g., Jira, ServiceNow)Monitoring and observability tools (e.g., Prometheus, ELK)GRC platforms and reporting tools

Principles & goals

Define clear responsibilities for risk identification and control.Combine quantitative and qualitative methods for risk assessment.Establish continuous monitoring and regular testing.
Run
Enterprise, Domain

Use cases & scenarios

Compromises

  • Missing or incorrect data leads to wrong assessments.
  • Overemphasis on metrics can overlook qualitative risks.
  • Unclear responsibilities delay escalations.
  • Combine qualitative assessments with quantitative metrics
  • Conduct regular simulations and drills
  • Transparent communication and traceable reporting

I/O & resources

  • Process documentation and workflow descriptions
  • Incident and loss history
  • SLA agreements and contract terms
  • Risk catalog and prioritization
  • Control matrix and responsibility assignment
  • Monitoring dashboards and reports

Description

Operational risk covers losses from failed processes, systems, people, or external events. The concept focuses on identifying, assessing and managing non-financial risks at organizational level. Metrics and regular tests validate controls.

✔

  • Reduction of unexpected losses through proactive management.
  • Improved resilience and business continuity.
  • Better decision-making through metrics and reporting.

✖

  • Not all risks can be fully quantified.
  • Effort for data preparation and metrics can be high.
  • Success depends strongly on culture and accountability.

  • Number of significant incidents

    Counts incidents that exceed defined impact thresholds.

  • Mean time to recover (MTTR)

    Average time to restore critical services after an incident.

  • Control effectiveness (pass/fail rate)

    Measure of how often controls perform as expected.

Bank: loss from process failure

Incorrect processing caused credit losses; adding controls reduced the risk.

IT provider: outage due to faulty deployment

Rollback procedures and automated tests shortened recovery time drastically.

Insurer: internal fraud

Improved segregation of duties and monitoring detected and prevented further cases.

1

Initial risk identification and creation of a risk catalog

2

Define metrics, controls and responsibilities

3

Introduce monitoring, tests and regular reviews

⚠️ Technical debt & bottlenecks

  • Legacy systems without telemetry hinder incident analysis
  • Incomplete data models for incident and loss data
  • Missing automated tests for critical recovery steps
Data quality and availabilitySkill gaps in risk analysisThird-party dependencies
  • Insuring all risks broadly instead of reducing them through processes
  • Monitoring creates many alerts without escalation rules
  • Controls are documented but not tested
  • Confusing operational risks with strategic or credit risks
  • Focusing only on rare extreme scenarios instead of frequent weaknesses
  • Excessive process complexity prevents practical implementation
Risk management and governance knowledgeData analysis and monitoring skillsProcess analysis and organizational change management
Availability of critical systemsTraceable audit and reporting pathsScalable monitoring and alerting
  • • Regulatory requirements and reporting obligations
  • • Limited resources for monitoring tools
  • • Legacy systems with poor observability