Beacon Data Normalization

Beacon Data Normalization is a security data pipeline component that transforms raw, source-specific logs into structured, schema-aligned data for use in SIEMs, data lakes, warehouses, and cloud storage. The product addresses the challenge of inconsistent log formats across security data sources by applying AI-powered field mappings that are validated by human experts. It ships with pre-built mappings for hundreds of log sources and supports industry-standard schemas including ECS (Elastic Common Schema), OCSF (Open Cybersecurity Schema Framework), ASIM (Advanced Security Information Model), and CIM (Common Information Model), as well as SIEM-native and custom schemas. Core capabilities include: - In-stream normalization: Data is normalized as part of a unified ingestion pipeline, with no post-processing or added latency. - Schema drift detection: When upstream vendors change log formats, add fields, or deprecate others, Beacon detects the change and adapts mappings automatically to prevent silent detection failures. - Multi-destination routing: Normalized data is routed to multiple destinations (SIEM, data lake, data warehouse, cloud storage) in the format each destination expects, without requiring re-transformation. - Unsupported source onboarding: Sources not natively supported by a SIEM can be mapped into existing schemas, enabling existing detection content to apply without custom parser development. - Data volume reduction: The product includes optimization capabilities, with one customer reporting VPC flow log reduction to 5% of original size. The product is positioned as a data foundation layer for security operations teams and AI-based security workflows, reducing engineering overhead associated with parser maintenance and log format changes.