Vega Secures $120M in Series B Funding for AI-Driven Security Operations

A New York-based security analytics startup, founded by the former research lead at Granulate, has secured $120 million to develop an artificial intelligence-native operating model for security operations. The funding, led by Accel, aims to enhance Vega’s capabilities in analytics, detection engineering, and threat hunting.

Shay Sandler, co-founder and CEO of Vega, stated that this funding would allow the company to expand into AI-driven triage and investigations, enabling a comprehensive approach to security operations. He emphasized the importance of contextualized data for AI’s effectiveness, noting that without accessible enterprise information, meaningful outcomes are unattainable.

Vega, established in 2024, has now raised a total of $185 million, following a $65 million Series A round in September. Sandler, who has a strong background in security research with nearly five years in Unit 8200 of the Israeli Military Intelligence and a tenure at Granulate before its acquisition by Intel, has consistently guided Vega’s vision since its inception.

Distributed Analytics and AI Operations

The recent funding will facilitate investment in various areas, including product development, AI research, and broader deployment models. Sandler noted that Accel’s ongoing support indicates a commitment to building a long-term company rather than pursuing a quick exit strategy.

Sandler highlighted the significant market opportunity, asserting that enterprises are eager for innovative solutions which prompt Vega to accelerate its product development and market presence. Instead of requiring customers to centralize their data, Vega allows analytics and AI agents to function across distributed data sources, thereby enhancing the detection and reporting processes based on comprehensive organizational contexts.

Integration for Enhanced Security Operations

Vega’s strategy focuses on detection engineering and threat hunting based on the understanding that prevention alone is inadequate. Organizations often struggle to establish meaningful detections due to architectural limitations, which can lead to decreased visibility and fewer alerts, according to Sandler.

The aim of Vega is to enable early detection of threats, improving organizational security by addressing vulnerabilities before they escalate into incidents. Traditional models typically separate detection and triage functions, hampering the potential for learning and adaptation. Vega’s approach promotes a continuous cycle, wherein insights gained from investigations enhance future detections.

This integration allows organizations to mature in their security posture, ensuring they do not start from scratch with each infrastructure shift. Vega’s model supports clients in maintaining their data wherever it resides, thereby simplifying compliance for industries with stringent regulations, such as finance and healthcare. This strategy minimizes resistance from security teams who might be deterred by the complexity and time involved in large-scale migrations.

The conventional security operations landscape often incurs delays and complications associated with migration, inhibiting a quick achievement of AI-native outcomes. Vega seeks to operationalize data in existing storage systems, enabling organizations to maximize their environments effectively and achieve value without the usual overhead.