Modern enterprises generate vast amounts of security data across cloud services, storage platforms, and internal systems. Yet many legacy security tools still require organizations to centralize that data before threats can be detected. This process can be slow, expensive, and difficult to manage as cloud adoption expands and data volumes surge. Vega Security, an AI focused cybersecurity startup, is aiming to change that model by bringing detection and response capabilities directly to where the data resides.
Moving Detection Closer To The Data
Vega Security has raised 120 million dollars in a Series B funding round led by Accel, with participation from Cyberstarts, Redpoint, and CRV. This Vega Security funding nearly doubles the company’s valuation to 700 million dollars and brings total funding to 185 million dollars. The company plans to use the capital to enhance its security operations suite, grow its commercial team, and expand into new global markets.
The company’s approach centers on running security analytics directly within cloud services, data lakes, and existing storage systems. Instead of forcing enterprises to copy and centralize massive datasets into a single platform, Vega enables detection and response processes to operate where the information already exists. This model aims to reduce costs, improve speed, and simplify deployment in complex cloud environments.
Shay Sandler, co founder and chief executive officer of Vega, argues that traditional security information and event management systems have struggled to keep pace with modern data growth. According to him, the model of centralizing logs and telemetry before analysis can introduce operational friction and increase exposure in dynamic cloud infrastructures.
Vega’s platform is designed to help organizations achieve incident response readiness without migrating data or redesigning existing architectures. The system focuses on enabling AI driven detection across distributed environments, supporting enterprises that operate across multiple cloud providers and on premises systems. The latest Vega Security funding will support further development of these capabilities.
Challenging Legacy Security Platforms
The cybersecurity market for security information and event management has been dominated for years by established vendors such as Splunk. However, the rapid growth of AI workloads and cloud adoption has increased pressure on centralized systems that must process expanding data streams.
Investors say Vega’s distributed detection model represents a different operating framework. Instead of consolidating data first and analyzing it later, the company seeks to embed analytics within existing environments. This approach, strengthened by the recent Vega Security funding, can reduce the need for large-scale data movement and potentially accelerate threat detection timelines.
Vega was founded two years ago and has grown to about 100 employees. Sandler previously worked in cybersecurity roles in the Israeli military and later helped build Granulate, a company acquired by Intel in 2022. After spending time at Intel, he launched Vega with a focus on modernizing enterprise detection and response.
The company reports signing contracts worth millions with banks, healthcare organizations, and Fortune 500 firms. Some of its customers operate heavily in cloud environments, where data distribution is common and traditional centralization models can be difficult to scale.
Vega positions its platform as AI native, meaning it is designed from the start to support automated analysis, detection, and response workflows across distributed systems. The goal is to allow enterprises to deploy the system quickly without lengthy migrations or major architectural changes.
As cyber threats continue to evolve and enterprise data footprints expand, vendors are experimenting with new approaches to visibility and response. Vega Security funding signals investor confidence in distributed detection models that seek to align security operations more closely with modern cloud infrastructure.
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