01
Risk Fusion
Combines security events, incident history and operational context into one explainable risk view.
Public MVP Preview
Retail risk becomes clearer when signals are fused into decisions.
SIRFE — Sentinel Intelligent Risk Fusion Engine — is being designed as a vendor-neutral retail security intelligence layer that correlates events, calculates contextual risk, supports incident decisions and preserves the evidence trail behind those decisions.
7
Architecture layers
1
Unified risk view
MVP
Current public status
SIRFE Fusion View
Illustrative management preview
Risk score
84
Elevated
Signals fused
05
Multi-source
Open incidents
03
Prioritised
Evidence state
Ready
Auditable
Decision trace
Multiple signals indicate a higher-priority retail security event.
84
ContextConfidenceEvidence
Platform
Built around fusion, not another isolated detector.
SIRFE is intended to sit above existing security and operational systems, normalising signals and turning them into a common incident, risk and evidence model.
02
Decision Intelligence
Turns correlated signals into prioritised incidents, recommended actions and auditable decision traces.
03
Evidence Governance
Supports evidence custody, retention, controlled disposal, policy oversight and production release controls.
04
Vendor-Neutral Integration
Designed as a fusion layer that can sit above existing cameras, event sources and future retail-system integrations.
Architecture
Seven layers from signal to governed action.
The architecture separates source ingestion, correlation, decisioning and evidence controls so SIRFE can evolve without being tied to a single camera or retail technology provider.
Layer 1
Sources
Layer 2
Event Fabric
Layer 3
Risk Graph
Layer 4
Fusion Engine
Layer 5
Decision Policy
Layer 6
Evidence
Layer 7
Learning & Governance
Management demo
Public product presence. Private operational access.
This public page is suitable for demonstrating that SIRFE has an internet-visible product presence. The operational interface remains protected behind SIRFE authentication and should use synthetic or approved demonstration data in public-facing MVP environments.