Vega Security Raises $120M Series B to Rethink Where Enterprise Threat Detection Runs

Vega Security closed a sizable $120 million Series B that positions the two-year-old company as a challenger to legacy SIEMs that require moving large volumes of telemetry into centralized systems. The round, led by Accel with participation from existing backers, nearly doubled Vega’s valuation and materially extended runway for engineering and commercial efforts. Vega’s platform is designed to execute detection and response inside cloud storage, data lakes and other existing enterprise stores so analytics and enforcement can run without forcing costly, time-consuming migrations. That architecture reduces ingestion fees and shortens implementation cycles—an attractive value proposition for cloud-first organizations that are strained by exploding data volumes and slow, centralized pipelines. Vega’s founding team brings deep operational experience in infrastructure and security, which has helped secure early multi-million-dollar contracts across banking, healthcare and large retail customers that heavily leverage cloud services. Technically, the in-place model lowers storage and processing overhead and can reduce exposure windows by enabling continuous analytics where telemetry already resides. Commercially, the model sidesteps a common blocker—customers’ reluctance to rework where critical logs are retained—and shortens the rationale-to-purchase timeline for teams prioritizing rapid detection and lower running costs. However, Vega must overcome incumbent entrenchment, procurement conservatism in regulated industries, and the complexity of integrating with heterogeneous cloud and on-prem systems. Performance consistency, manageable false-positive rates, and enterprise-grade policy and compliance controls will be essential for broader adoption. The financing gives Vega headroom to refine its AI-native stack, hire experienced sales teams, and expand internationally. This timing also aligns with a broader security market trend toward runtime observability and controls for AI-driven and automated systems—enterprises increasingly seek solutions that monitor behavior at decision time rather than relying solely on centralized log aggregation or model-level fixes. If Vega can demonstrate reliable detection efficacy across diverse storage backends with low integration friction, its approach could push incumbents to adopt hybrid models that support in-place analytics. For now, the new capital strengthens Vega’s ability to prove its thesis in enterprise production environments while highlighting the execution risks inherent to scaling a security platform across complex customer landscapes.