Glow, a cybersecurity startup founded by former Meta and Snowflake executives, has emerged from stealth as a unicorn, convinced that artificial intelligence is reshaping the way companies protect employee devices.
The Palo Alto-based startup announced Wednesday that it has raised $180 million in an all-stock Series A funding round valued at $1.2 billion, backed by Sequoia Capital, Cyberstarts, Greenoaks, and Redpoint Ventures, with participation from Index Ventures, Swish Ventures, Lux Capital, Operator Collective, and Holly Ventures. This investment makes Glow one of the latest cybersecurity startups to achieve unicorn status before releasing revenue metrics.
As more companies deploy AI tools and attackers increasingly use generated AI to automate phishing, develop malware, and launch more sophisticated cyberattacks, companies are rethinking how they protect endpoints, from employee laptops to servers and other connected devices. Concerns have intensified since Anthropic announced its Mythos AI model, which the company says has demonstrated advanced capabilities for identifying and exploiting software vulnerabilities, sparking a broader discussion about AI-powered cyberattacks. At Glow, we believe this change requires a new approach to endpoint security.
Founded in 2025, Glow builds an endpoint security platform that enables enterprises to monitor and control the software, AI agents, and developer tools running on employee devices. According to the company, the platform uses specialized AI agents to continuously map enterprise environments, assess risks and enforce security policies in real-time.
“If you look back at the last 10 years, everything was moving to the cloud and SaaS, and suddenly AI was reaching the endpoint in a way we had never seen before,” co-founder and CEO Roy Tiger (pictured above, center) said in an interview.
Tiger, a former Meta vice president of engineering, co-founded Glow with former Snowflake head of cybersecurity strategy Omar Singer (pictured above, left), former Claroti vice president of research and development Ofir Ali (pictured above, right), and former Meta engineering lead Arnon Joseph. The startup’s leadership team also includes chief operating officer Emily Heath, who previously served as chief information security officer at United Airlines and DocuSign, served on the board of Wiz through Google’s $32 billion acquisition, and was previously a partner at CyberStarts.
Despite just coming out of stealth, Grow said it already has paying customers across industries including healthcare, retail and financial services. However, the company refused to disclose the customer’s name and number. Tiger said the startup’s typical deployment spans tens of thousands of employee devices across a global organization.
To power the platform, Glow uses AI models from Anthropic and Google’s Gemini via Amazon Bedrock, while also building its own software to provide enterprise context to the models and improve reliability of security tasks, Tiger told TechCrunch.
Tiger said Glow’s platform has already stopped malicious npm packages (third-party software components used to build applications) from being installed in customer environments, identified AI agents attempting to ingest such software, and detected employee devices that were missing or operating in a degraded manner with endpoint detection and response tools.
Glow enters a crowded endpoint security market dominated by companies like CrowdStrike, Microsoft, SentinelOne, and Palo Alto Networks. Tiger said that while existing endpoint detection and response products primarily focus 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 has nearly 100 employees, about 70% of whom are in Israel and the rest in the United States. It remains to be seen whether AI-native endpoint security platforms will become a distinct category, as enterprises are just beginning to grapple with the security implications of increasingly capable AI models.
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