Glow raises $180 million at a $1.2 billion valuation to secure the AI agents quietly taking over corporate laptops

Glow, an endpoint security startup, raised $180 million in Series A funding at a $1.2 billion valuation to protect corporate laptops from unapproved AI agents. The company, founded by former Meta VP Roi Tiger and backed by Sequoia Capital, Cyberstarts, Greenoaks, and Redpoint Ventures, aims to intercept AI-driven risks before they become incidents, as Verizon's 2026 Data Breach Investigations Report found that regular employee use of AI tools on corporate devices rose from 15% to 45% in one year.

Glow has raised $180 million at a $1.2 billion valuation because enterprise AI has moved onto the laptop faster than security teams can police it. The bet is simple: if agents are going to read files, pull code, and touch company systems, endpoint security has to stop waiting until after something goes wrong. Glow came out of stealth on July 22, 2026 with the kind of launch most security startups spend years trying to earn: a $180 million Series A, a $1.2 billion valuation, and a founder whose past makes the pitch more interesting, not less. That's quite an entrance. Axios Pro reported that Glow, an endpoint security company, raised the money at a $1.2 billion post-money valuation. SecurityWeek and Glow's own launch post listed Sequoia Capital, Cyberstarts, Greenoaks, and Redpoint Ventures as lead investors, with Index Ventures, Swish Ventures, Lux Capital, Operator Collective, and Holly Ventures also participating. That's a crowded cap table. It also tells you the market isn't treating AI endpoint risk as a side issue. The company was founded in 2025 by CEO Roi Tiger, CTO Omer Singer, and VP of R&D Ophir Arie. Tiger was a Meta VP of engineering and co-founded Onavo, the mobile analytics company Facebook bought in 2013. Singer previously led cybersecurity strategy at Snowflake. Arie was previously VP of R&D at Claroty, according to SecurityWeek. You don't need to romanticize that team. The point is more practical: these are people who have worked at scale in device data and enterprise security, where vague product language doesn't survive long. Why endpoints are back For years, enterprise security attention drifted toward cloud, identity, SaaS, and data platforms. That made sense. Work moved there. But AI agents pull part of the problem back to the endpoint, where your employees actually install tools, run scripts, connect browser extensions, open developer environments, and paste data into products security may never have approved. That is the sell. Glow argues that the old endpoint model is too reactive for this moment. CrowdStrike, SentinelOne, Microsoft, and Palo Alto Networks built important businesses by watching devices for malicious behavior and helping teams respond. That still matters. But an AI agent isn't always malware. It may arrive through a legitimate app, run with a user's permissions, read local files, call outside services, touch internal systems, and do all of this before anyone in security has decided whether it belongs there. According to Verizon's 2026 Data Breach Investigations Report, regular employee use of AI tools on corporate devices rose from 15% to 45% in one year. Verizon also said 67% of those users accessed AI services through non-corporate accounts. If you're running security, that number should bother you. It means the exposure isn't just a theoretical agent doing something clever in a demo. It's ordinary employees using ordinary tools in ways that move company data outside normal controls. Glow's product tries to intercept that problem earlier. The company says its AI agents create a live inventory across devices and people and software, assess risk against company policy, and enforce controls before risky software becomes an incident. Its site also claims the average organisation has more than 12,000 unique pieces of software, that 67% of software is invisible to existing tools, and that 30% of AI agents operate with no guardrails. Some of those numbers are company claims, so treat them that way. Still, the direction is clear. The hard sell A $1.2 billion valuation on launch day is not proof that the product works in messy enterprise environments. It's proof that investors believe the category is forming quickly. Glow says it already has enterprise customers in healthcare and financial services and retail, but it hasn't named them in the funding coverage. SecurityWeek reported that the company plans to use the funding to expand its US go-to-market work and grow Glow Labs, its research division. Here's the thing: prevention sounds cleaner than detection until it starts blocking work. Security teams know this. Developers know it even faster. If Glow is going to sit close to the device and decide what software or AI agents can run, it has to be accurate enough that employees don't spend their day fighting the system. False positives are not a small operational nuisance. They are how people learn to route around security. Tiger's Onavo history also gives the story a sharp edge. Onavo became controversial because it gave Meta visibility into how people used rival apps, and Apple pushed back before Meta shut it down in 2019. Glow's pitch is almost the mirror image: visibility over software should help companies reduce risk, not quietly watch users for competitive advantage. That distinction has to be earned in product design, controls, and customer trust. The timing helps. Black Hat USA 2026 starts in Las Vegas on August 3, and Glow is already advertising its presence there. Security buyers will hear the pitch in person: AI changed the endpoint, and waiting for alerts isn't enough. Frankly, that argument is right. The open question is whether Glow can turn it into a product security teams trust when real employees and real company data are moving faster than any policy document can keep up. 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