From Agentic SIEM to Digital Twin: Lakehouse-Native Cybersecurity Red-Team Simulation

Modern security platforms collect massive telemetry but remain reactive. We present a Lakehouse-native cybersecurity Digital Twin architecture powered by an Agentic SIEM that transforms raw logs into simulation-ready intelligence. An AI-driven SIEM parses and normalizes heterogeneous security data into a governed Lakehouse schema. This structured foundation enables construction of a Digital Twin that models enterprise assets, identities, privileges, and network relationships directly from telemetry. AI agents then simulate realistic attack chains, including privilege escalation, lateral movement, and credential harvesting, validating each step against Lakehouse-derived state. Instead of estimating risk from alerts, the system verifies exploitability with confidence scoring based on data coverage and fidelity. We demonstrate how the Lakehouse becomes an active simulation engine for measurable cyber risk validation, not just a passive data store.