Glow, an AI-native cybersecurity startup, emerges from stealth with $180M funding and $1.2B valuation

Glow, a cybersecurity startup founded by former Meta and Snowflake executives, emerged from stealth as a unicorn, betting that artificial intelligence is reshaping how enterprises secure employee devices.

The Palo Alto-headquartered startup on Wednesday said it raised $180 million in an all-equity Series A funding round that valued it at $1.2 billion, with backing from Sequoia Capital, Cyberstarts, Greenoaks, and Redpoint Ventures, alongside participation from Index Ventures, Swish Ventures, Lux Capital, Operator Collective, and Holly Ventures. The investment made Glow one of the latest cybersecurity startups to achieve unicorn status before publicly disclosing revenue metrics.

As more enterprises deploy AI tools and attackers increasingly use generative AI to automate phishing, develop malware, and launch more sophisticated cyberattacks, companies are rethinking how they secure endpoints — from employee laptops to servers and other connected devices. Concerns have intensified since Anthropic unveiled its Mythos AI model, which the company said demonstrated advanced capabilities in identifying and exploiting software vulnerabilities, prompting broader debate over AI-assisted cyberattacks. Glow is betting that this shift requires a new approach to endpoint security.

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 said 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 who served on the board of Wiz through its $32 billion acquisition by Google and was previously a partner at Cyberstarts.

Even though it has only just emerged from stealth, Glow said it already has paying customers across industries including healthcare, retail, and financial services. However, it declined to disclose customer names and numbers. The startup’s typical deployments, Tiger said, 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, while building its own software to provide the models with enterprise context and improve their reliability for security tasks, Tiger told TechCrunch.

Glow’s platform, Tiger said, has already prevented malicious npm packages, third-party software components used to build applications, from being installed in customer environments, 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.

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, whereas 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% of whom are in Israel and the remainder in the U.S. 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.