"Modern enterprises generate enormous amounts of security data, but legacy tools like Splunk still require companies to store all of it in one place before they can detect threats - a slow and costly process that's increasingly breaking down in cloud environments where volumes are exploding and data lives everywhere. AI cybersecurity startup Vega Security wants to flip that approach by running security where the data already lives, implementing in cloud services, data lakes, and existing storage systems."
"Shay Sandler, co-founder and CEO of Vega, told TechCrunch that the current operating model of the SIEM (security information and event management) - the dominant technology in this domain for the last two decades - is not only "crazy expensive," but is also increasingly causing AI-native security operations to fail. In complex cloud environments, he says, the current model often increases exposure to threat actors."
Modern enterprises produce massive volumes of security data, and legacy SIEM tools require centralized storage before detection, creating slow, costly, and inflexible operations. Vega Security runs detection where data resides—in cloud services, data lakes, and existing storage—to avoid full ingestion into a single system and to support AI-native detection and response at scale. The company raised a $120 million Series B led by Accel, valuing Vega at $700 million and bringing total funding to $185 million. Funding will accelerate development of an AI-native security operations suite, expand go-to-market capabilities, and support global growth. The co-founder and CEO has Israeli military cybersecurity experience and co-founded Granulate.
Read at TechCrunch
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