San Francisco–based cybersecurity startup Scanner announced that it has raised $22 million in a Series A funding round led by Sequoia Capital, with participation from CRV and Mantis VC. The funding will support the company’s mission to build a modern data layer for security teams operating in the era of AI-driven threat detection and response. Founded to address the limitations of traditional security logging systems, Scanner provides a cloud-native security data lake designed to make vast amounts of log data instantly searchable while maintaining the cost advantages of object storage.
Security teams frequently face a difficult tradeoff: paying high SIEM ingestion costs for searchable data retained only for short periods or storing logs cheaply in object storage where queries can take hours or even days. Scanner addresses this challenge by introducing a new indexing architecture optimized for object storage, enabling organizations to rapidly query years of security data without maintaining costly always-on infrastructure. The platform dynamically scales compute resources to run queries and automatically scales down afterward, allowing teams to pay only for the insights they need. As cyber threats become increasingly complex, the ability to search and analyze historical log data has become essential for security operations.
Scanner’s platform enables organizations to investigate threats across multiple data sources and long time horizons, helping analysts detect attackers earlier and respond more effectively. The technology is already used by a growing number of enterprise teams, including companies such as Notion, Ramp, and BeyondTrust, to analyze security logs and investigate incidents across large-scale environments.
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“Scanner gave us months of searchable history instead of two weeks. When new threats emerge, we build detections and search years of logs for IOCs very rapidly. Both are game-changers for security at scale.” Brandon Ledyard, Detection Engineer, Ramp. The company also reports a surge in adoption of AI-driven security workflows.
Over the past several months, AI agents have become some of the most active users of the Scanner platform, continuously querying large volumes of log data to hunt for threats, triage alerts, and assist with incident investigations. By providing fast, scalable access to massive datasets, the platform allows AI agents to correlate signals across systems and generate context-rich insights for human analysts. This emerging “agentic” model of security operations is helping organizations reduce investigation times and improve operational efficiency.
Investors backing the round include experienced cybersecurity leaders and industry figures, reflecting strong confidence in Scanner’s vision of combining AI automation with scalable data infrastructure. “Security teams generate massive amounts of data but can only afford to search a fraction of it. Scanner has built a fundamentally new approach to this problem, which enables companies to move into the agentic era of cybersecurity.
AI is notoriously data hungry, and Scanner is the only technology on the market today that manages security data at AI scale.” Bogomil Balkansky, Partner, Sequoia Capital. With the new funding, Scanner plans to expand its platform capabilities, accelerate product development, and bring its high-performance security data infrastructure to more enterprises seeking faster, more cost-effective threat detection and investigation capabilities.
Source: Scanner
