7.2 Predictive Risk Inference

Leveraging historical and real-time telemetry, the AI Engine forecasts potential failures, slashing threats, and performance degradations before they occur: • Anomaly Detection: Unsupervised learning models automatically identify outliers—CPU spikes, memory exhaustion, peer desyncs, or RPC error bursts—then surface contextual alerts. • Slashing Risk Scoring: Supervised classifiers trained on past slashing events, network congestion patterns, and mis-configuration signatures generate a dynamic risk score (0–100) for each validator. • Downtime Probability: Time-series forecasting techniques (e.g., ARIMA, LSTM) estimate the likelihood of node outages within the next 6– 24 hours, enabling scheduling of pre-emptive maintenance windows. • Performance Optimization Insights: Reinforcement-learning agents analyze workload trends (e.g., CPU utilization vs. block‐production latency) and recommend hardware tier adjustments or peer-set reconfigurations to optimize throughput and cost. All risk metrics are written back into the TSDB and exposed via the API, SDK, and dashboard widgets—allowing operators to build custom queries or integrate with external tools.