3Ο statistical thresholds
Filter noise from genuine deviations before any human or autonomous action.
π¬π§ Edinburgh
AI Anomaly Detection in Edinburgh
AI anomaly detection uses statistical baselines and machine learning to identify deviations from normal infrastructure behavior. Modern systems score signals above 3Ο as anomalous and trigger a remediation pipeline, not just an alert. SentienGuard detects infrastructure incidents, selects a remediation playbook, executes controlled fixes, verifies recovery and records the evidence.
Local operating context
Edinburgh fintech (banking, asset management, insurtech) operates under PRA operational-resilience rules. Autonomous remediation paired with immutable logs satisfies the impact-tolerance evidence requirement.
A representative 500-node Team deployment is approximately Β£18,960/year. Pro tier estimate: 500 nodes Γ $4/endpoint/month annual commit. FX rates as of 2026-05. See /pricing for canonical USD pricing.
Capabilities
Multi-signal correlation
Metrics + logs + events fused into one incident hypothesis.
Triggers RAG selection
Anomaly embedded into vector β match playbook β execute.
Low false-positive rate
Confidence scoring keeps the autonomous path tight.