Key Takeaway
Every AI system should have a defined graceful degradation path that provides reduced but functional service when the primary model or provider is unavailable. This guide covers five AI-specific disaster scenarios with RTO/RPO targets, failover procedures, and recovery runbooks for each.
Prerequisites
- An existing business continuity or disaster recovery framework
- Inventory of all AI systems with their dependencies (providers, models, data stores, infrastructure)
- Model versioning and artifact storage with backup capabilities
- Understanding of which AI features are business-critical vs. best-effort
- On-call procedures and incident management infrastructure
AI Disaster Scenarios Are Different
Traditional disaster recovery plans assume that recovery means restoring the same application to the same state. AI disaster recovery adds scenarios that have no traditional equivalent: your model provider goes down (your code is fine but the model is unreachable), your model is corrupted (the application is running but producing wrong results), your training data is lost (the running model still works but you cannot retrain it), or GPU availability evaporates (training and scaling become impossible even though current serving continues). Each scenario requires different recovery strategies and different RTO/RPO targets.
RTO/RPO Framework for AI Systems
RTO (Recovery Time Objective) and RPO (Recovery Point Objective) must be defined separately for model serving and model training. Serving RTO is how long you can be without any AI inference capability. Training RPO is how much training data and progress you can afford to lose. These targets vary by system criticality: a customer-facing recommendation engine has different RTO requirements than an internal analytics model.
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