A cybersecurity startup called Glow, founded by former executives from Meta and Snowflake, has emerged from stealth with unicorn status. The company believes artificial intelligence is fundamentally changing how businesses secure employee devices.
The Palo Alto based startup announced Wednesday that it raised $180 million in an all equity Series A funding round. The round valued the company at $1.2 billion. Investors include Sequoia Capital, Cyberstarts, Greenoaks, and Redpoint Ventures. Index Ventures, Swish Ventures, Lux Capital, Operator Collective, and Holly Ventures also participated. The investment makes Glow one of the latest cybersecurity startups to reach unicorn status without first disclosing revenue figures.
The AI security challenge
As more companies deploy AI tools and attackers use generative AI to automate phishing, develop malware, and launch more sophisticated cyberattacks, organizations are rethinking how they secure endpoints. Endpoints include employee laptops, servers, and other connected devices. Concerns have grown since Anthropic unveiled its Mythos AI model, which the company said showed advanced abilities in identifying and exploiting software vulnerabilities. That development sparked broader debate about AI assisted cyberattacks. Glow is betting that this shift demands a new approach to endpoint security.
Glow's platform and founding team
Founded in 2025, Glow is building an endpoint security platform that helps enterprises monitor and control the software, AI agents, and developer tools running on employee devices. The startup says the platform uses specialized AI agents to continuously map enterprise environments, assess risk in real time, and enforce security policies.
"If you think of the past decade, everything was moving to the cloud and SaaS. Suddenly, AI lands on the endpoint in a way we've never seen," co-founder and chief executive Roi Tiger said in an interview.
Tiger, a former Meta vice president of engineering, co founded Glow alongside former Snowflake cybersecurity strategy head Omer Singer, former Claroty vice president of research and development Ophir Arie, and former Meta engineering leader Arnon Joseph. The startup's leadership team also includes chief operating officer Emily Heath, a former chief information security officer at United Airlines and DocuSign. Heath served on the board of Wiz through its $32 billion acquisition by Google and was previously a partner at Cyberstarts.
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Early customers and AI models
Even though it has only just emerged from stealth, Glow says it already has paying customers across industries including healthcare, retail, and financial services. The startup declined to disclose customer names or numbers. Tiger said typical deployments span tens of thousands of employee devices across global organizations.
To power the platform, Glow uses AI models from Anthropic and Google's Gemini through Amazon Bedrock. The company also builds its own software to provide the models with enterprise context and improve their reliability for security tasks, Tiger told TechCrunch.
Tiger said Glow's platform has already prevented malicious npm packages from being installed in customer environments. Npm packages are third party software components used to build applications. The platform also identified AI agents attempting to pull in such software and detected employee devices where endpoint detection and response tools were missing or operating with reduced functionality.
Competitive landscape
Glow enters a crowded endpoint security market dominated by companies including CrowdStrike, Microsoft, SentinelOne, and Palo Alto Networks. Tiger said existing endpoint detection and response products focus primarily on detecting threats after they emerge. Glow is designed to prevent risky software, AI agents, and developer tools from entering enterprise environments in the first place.
The startup employs nearly 100 people. About 70% are in Israel and the remainder in the United States. Whether AI native endpoint security platforms become a distinct category remains to be seen, as enterprises are only beginning to grapple with the security implications of increasingly capable AI models.