COMPEL Glossary / disaster-recovery
Disaster Recovery
Disaster recovery encompasses the plans, processes, and technical infrastructure for restoring AI systems, data, and services after a catastrophic failure such as data center outages, major security breaches, data corruption, or natural disasters.
What this means in practice
Key metrics include Recovery Time Objective (RTO, how quickly systems must be restored) and Recovery Point Objective (RPO, how much data loss is acceptable). For organizations that have embedded AI into critical business processes, disaster recovery planning must address AI-specific scenarios including model weight corruption, training data loss, feature store failures, and the restoration of complex ML pipeline states. In COMPEL, disaster recovery is part of the operational resilience assessment during Calibrate and connects to the business continuity planning within the Governance and Technology pillars.
Why it matters
Organizations that embed AI into critical business processes face unique disaster recovery challenges that traditional IT recovery plans do not address. AI-specific scenarios like model weight corruption, training data loss, and ML pipeline state restoration require specialized recovery procedures. Without AI-aware disaster recovery, organizations risk extended outages of AI-dependent operations that can cascade into broader business disruption.
How COMPEL uses it
Disaster recovery is assessed as part of the operational resilience evaluation during Calibrate, specifically evaluating AI-specific recovery capabilities under the Technology and Governance pillars. During Model, recovery objectives (RTO and RPO) are defined for each AI system based on business criticality. The Produce stage implements recovery infrastructure, and the Evaluate stage tests recovery procedures through tabletop exercises.
Related Terms
Other glossary terms mentioned in this entry's definition and context.