Introducing EvidenceForge: Synthetic security logs that don’t look (as) fake

EvidenceForge generates high-quality, realistic, and consistent datasets across multiple log formats, enabling teams to effectively train personnel and validate detection models without the need for complex manual simulations.

Security teams need high-quality, labeled datasets to train threat hunters and incident responders, validate detection logic, and develop robust analytic models.   EvidenceForge helps teams overcome the limitations of anonymized or stale public datasets, while avoiding the cost and complexity of setting up real infrastructure and performing manual attack simulations to create their own.

The tool incorporates sophisticated timing models and assigns specific roles to users and systems, generating realistic malicious activity, background noise, and “red herrings” to optimize data realism.   The tool generates correlated logs across 20+ Windows, Linux, and network monitoring formats using a canonical event model that ensures causal and temporal consistency.