Glow has emerged from stealth with a $1.2 billion valuation and a fresh $180 million Series A, pitching itself as an endpoint security company built for a world where AI tools and AI agents are spreading across employee devices faster than many security teams can track them.
A unicorn debut with heavyweight backers
The Palo Alto-headquartered startup said Wednesday that it raised the all-equity round from Sequoia Capital, Cyberstarts, Greenoaks, and Redpoint Ventures, with additional participation from Index Ventures, Swish Ventures, Lux Capital, Operator Collective, and Holly Ventures. The financing gives Glow unicorn status before it has publicly disclosed revenue metrics, placing it among a growing group of cybersecurity startups that have reached billion-dollar valuations early in their life cycle.
Glow was founded in 2025 by former Meta and Snowflake executives and is now positioning itself around a simple thesis: artificial intelligence is changing not just the threats enterprises face, but also where those threats live. As more organizations deploy AI tools internally and as attackers use generative AI to automate phishing, develop malware, and run more advanced campaigns, the company says traditional endpoint security needs to evolve.
Why Glow thinks endpoints need a reset
For years, endpoint security has largely centered on monitoring laptops, servers, and connected devices for malicious behavior after threats arrive. Glow argues that this model is increasingly insufficient when AI agents, developer tools, and software components can be introduced to employee devices at speed, sometimes with limited visibility from IT and security teams.
“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’s comments reflect a broader anxiety in the security industry: the more capable AI systems become, the more they can be used both by defenders and attackers. Concerns about AI-assisted cyberattacks have intensified since Anthropic unveiled its Mythos AI model, which the company said demonstrated advanced capabilities in identifying and exploiting software vulnerabilities.
Glow’s bet is that enterprises need a security layer that can respond earlier in the software lifecycle and more directly on the device, especially as employees adopt new tools faster than centralized security teams can review them.
What Glow says its platform does
The startup says it is building an endpoint security platform that helps enterprises monitor and control the software, AI agents, and developer tools running on employee devices. Rather than only flagging threats after they appear, Glow says it uses specialized AI agents to continuously map enterprise environments, assess risk in real time, and enforce security policies.
According to Tiger, the platform is designed to provide security teams with a more complete picture of what is actually operating across an organization’s devices. That includes software packages, AI agents, and other tools that may be installed by developers or employees outside the strict control of traditional endpoint management processes.
Glow says that approach is already producing results. The company said its platform has prevented malicious npm packages — third-party software components commonly 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.
Those claims point to a security model focused on prevention and configuration awareness, not just alerting and incident response. Glow says it is trying to stop risky software and tools from entering enterprise environments in the first place.
Customers, scale, and early traction
Even though Glow is only now stepping out of stealth, it said it already has paying customers in healthcare, retail, and financial services. The company declined to name those customers or disclose how many it has signed.
Tiger said typical deployments span tens of thousands of employee devices across global organizations. That suggests the startup is targeting large enterprises with distributed device fleets and complex software environments — the kinds of customers most likely to feel pressure from both AI-driven productivity gains and AI-driven attacks.
The company’s early traction matters because the endpoint security market is one of the most competitive segments in cybersecurity. Glow is entering a field dominated by companies including CrowdStrike, Microsoft, SentinelOne, and Palo Alto Networks. Those incumbents already have broad visibility across enterprise environments and long-standing relationships with security buyers.
Glow’s challenge is not just to prove that it can detect threats, but that it can offer a meaningful preventive layer that existing products do not. Tiger argued that current endpoint detection and response tools are still centered primarily on identifying threats after they emerge, while Glow is designed to keep risky software, AI agents, and developer tools out of the environment altogether.
The team behind the company
Glow’s founding team brings experience from several major enterprise technology and security companies. Tiger previously served as vice president of engineering at Meta. He co-founded the startup with Omer Singer, who led cybersecurity strategy at Snowflake; Ophir Arie, formerly vice president of research and development at Claroty; and Arnon Joseph, another former Meta engineering leader.
The leadership group also includes chief operating officer Emily Heath, a former chief information security officer at United Airlines and Docusign. Heath also served on the board of Wiz through its $32 billion acquisition by Google and was previously a partner at Cyberstarts.
That mix of operators and security veterans may help Glow navigate a market where buying decisions are shaped not just by technical claims but also by trust, compliance needs, and the ability to integrate with existing enterprise workflows.
How Glow is building its product
Glow told TechCrunch that it uses AI models from Anthropic and Google’s Gemini through Amazon Bedrock. The company is also building its own software to add enterprise context and improve reliability for security use cases.
That architecture reflects a common pattern among startups building on top of large AI models: the underlying models provide broad reasoning and detection capabilities, while proprietary software and data are used to make outputs more accurate, usable, and tailored to a specific business problem. In Glow’s case, that problem is endpoint security in environments where AI is increasingly embedded in day-to-day work.
By combining third-party models with its own context layer, Glow appears to be trying to balance speed of development with specialization. The startup’s pitch is that general-purpose AI can help security teams scale their visibility, but only if it is adapted to the particular realities of enterprise devices, software supply chains, and developer behavior.
What the funding signals for cybersecurity
Glow’s debut comes at a moment when cybersecurity investors continue to favor companies that claim to redefine categories rather than simply compete within them. A $180 million all-equity Series A is unusually large, especially for a startup that only began in 2025 and has not publicly disclosed revenue. The size of the round suggests investors believe the intersection of AI and endpoint security could support a substantial new market.
It also reflects confidence in the founding team and the broader narrative that AI is changing the endpoint in ways existing tools were not designed to handle. Whether AI-native endpoint security becomes a distinct category remains an open question, however. Enterprises are still in the early stages of understanding how capable AI models alter both the attack surface and the internal software environments they manage.
Glow is betting that the answer will be a new kind of endpoint platform, one that watches not only for malware and suspicious behavior, but for the software, agents, and tools that may create risk before an attack ever starts.
For now, the startup has plenty of capital, a notable customer list it is not yet ready to name, and a big claim to prove: that endpoint security in the AI era requires a fundamentally different approach.
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Source: Original report
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Last Modified: July 22, 2026 at 6:39 pm
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