The COMPEL Glossary Graph visualizes relationships between framework terminology, showing how concepts interconnect across domains, stages, and pillars. Term nodes cluster by pillar affiliation while cross-references reveal semantic dependencies — for example, how risk appetite connects to control effectiveness, model governance, and assurance requirements. This network representation helps practitioners navigate the framework vocabulary and understand that COMPEL terminology forms a coherent conceptual system rather than isolated definitions.
COMPEL Glossary / GL-56
Mean Time To Recovery (MTTR)
The average elapsed time from detection of an AI incident or SLO breach to restoration of the system to an operationally healthy state.
What this means in practice
MTTR is measured across the incident lifecycle — detect, triage, mitigate, recover — and is tracked separately for model-specific failures (drift, hallucination spikes) and infrastructure failures (latency, availability), providing a concrete reliability signal tied to user impact.
Context in the COMPEL framework
A core metric of the Reliability dimension. Captured in Evaluate and used during Learn to drive runbook and observability improvements.
Where you see this
Mean Time To Recovery (MTTR) is most commonly referenced when teams work across the Evaluate and Learn stages — especially within the Operational Readiness layer . It appears in governance artifacts, assessment instruments, and delivery playbooks wherever COMPEL is operationalized.
Related COMPEL stages
Related domains
Synonyms
mean time to recover , recovery time , MTTR
See also
- Trust & Performance Dimensions — The eight continuous-measurement axes against which every AI transformation is evaluated in COMPEL: Value, Reliability, Safety, Responsibility, Compliance, Security, Sustainability, and Adoption.
- Operational Readiness — The assessed capability of an organization to sustain AI operations across 10 interdependent dimensions: strategy alignment, governance maturity, operating model, workforce capability, data readiness, technology infrastructure, monitoring and observability, vendor dependency management, compliance readiness, and change and adoption.
- Governance Control — A defined mechanism — preventive, detective, or corrective — that enforces policy compliance, mitigates identified risks, or ensures operational integrity for AI systems.