Teams ship detection rules constantly, but few can answer whether an alert will fire before funds leave the system. This talk breaks down a simulation framework built to test candidate detection rules against a dataset of 46 historical exploit events using archive-node state-forking.

The session explores:

Rebuilding incidents block-by-block to evaluate detector precision and recall.

Establishing empirical detection deadlines based on observed exfiltration curves instead of arbitrary SLAs.

Managing false-positive budgets when automated remediation requires irreversible protocol pauses.

Addressing detection blind spots caused by zero-visibility private mempools.

While tested on blockchain datasets where ground truth is public, the simulation methodology, metric frameworks, and open-source harness apply directly to any real-time threat detection, telemetry, and automated alerting system.