Dependency Health The AI system depends on upstream services (data sources, feature stores, external APIs) and downstream consumers (deployer applications, reporting systems). Dependency health monitoring tracks the availability, latency, and error rates of these external touchpoints. A degradation in an upstream data source can corrupt the system’s inputs without triggering any model-level alert. Dependency monitoring covers every external integration documented in the system architecture (Module 3), with alerting thresholds calibrated to the dependency’s criticality. The failure modes for each dependency, and the system’s expected behaviour when a dependency is unavailable, are documented by the Technical SME in the PMM plan and tested periodically through chaos testing. For GPAI model dependencies (where the system relies on an external model API), dependency health monitoring extends to model behaviour: response quality, latency, and output distribution should be tracked alongside availability. Key outputs

• Per-dependency availability, latency, and error rate monitoring
• Criticality-calibrated alerting thresholds
• Failure mode documentation and periodic testing
• GPAI model dependency behaviour monitoring