Glow exits stealth with $180M to redefine endpoint security for the AI era

Cybersecurity startup Glow has come out of stealth with a $1.2 billion valuation after raising $180 million in an all-equity Series A, positioning itself as a new entrant in endpoint security built specifically for the age of AI. The Palo Alto company says it wants to stop risky software, AI agents and developer tools from ever reaching employee devices, rather than waiting to detect attacks after they begin.

The funding round, announced Wednesday, was led by Sequoia Capital and Cyberstarts, with Greenoaks and Redpoint Ventures also participating alongside Index Ventures, Swish Ventures, Lux Capital, Operator Collective and Holly Ventures. The deal makes Glow one of the newest cybersecurity unicorns to emerge before disclosing any revenue numbers, a sign of both investor appetite and the urgency many companies now feel around AI-driven threats.

Glow’s pitch is straightforward: enterprise security tools were built for a world where software largely moved to the cloud, but AI is now arriving on the endpoint itself, inside employee laptops, developer environments and server workflows. The company argues that this shift requires a more proactive approach to device security, one that can understand not just traditional software but also the autonomous tools and models increasingly used inside organizations.

Why Glow thinks endpoint security needs a reset

Glow believes the next wave of cyber risk is being created by the same AI tools companies are rushing to adopt. As businesses roll out chatbots, coding assistants and AI agents, attackers are also using generative AI to automate phishing, build malware and scale intrusions faster than before.

The startup’s thesis is that legacy endpoint detection and response products are still centered on spotting threats after they have already entered a system. Glow says that model is no longer enough when employees can install AI tools, developers can pull in third-party packages and agents can interact with sensitive systems at machine speed.

That concern has become more pronounced as AI systems show more advanced offensive potential. Anthropic’s recent disclosure of its Mythos model, which the company said was capable of identifying and exploiting software weaknesses, intensified debate over whether AI can lower the barriers to cybercrime and accelerate exploitation.

In that environment, Glow is betting enterprises will want controls that understand the full context of what is running on every device, and that can block unsafe software before it becomes a problem.

How does Glow’s platform work?

Glow says its platform continuously maps enterprise environments, evaluates risk in real time and enforces policies across employee devices. The system is designed to monitor the software, AI agents and developer tools running across laptops, servers and other connected endpoints.

Rather than functioning as a passive detector, the platform uses specialized AI agents to inspect devices and determine whether the activity, software or package being introduced creates an unacceptable risk. Glow says this gives security teams a way to catch dangerous behavior earlier in the software lifecycle.

The company told TechCrunch that it is using AI models from Anthropic and Google’s Gemini through Amazon Bedrock, while building its own software layer to add enterprise context and improve model reliability for security operations. That context layer is important, Glow argues, because raw model output is not enough for enterprise defense decisions where accuracy matters.

According to the startup, the platform has already stopped malicious npm packages from being installed in customer environments, flagged AI agents trying to bring in those packages and identified devices where endpoint detection tools were missing or performing poorly.

Who is behind Glow?

Glow was founded by a group of executives with deep experience in large-scale infrastructure, security and enterprise operations. Chief executive Roi Tiger previously served as a vice president of engineering at Meta, while co-founder Omer Singer led cybersecurity strategy at Snowflake.

They are joined by Ophir Arie, who previously held a vice president of research and development role at Claroty, and Arnon Joseph, another former Meta engineering leader. The startup’s leadership also includes chief operating officer Emily Heath, whose background spans information security leadership at United Airlines and DocuSign as well as a partner role at Cyberstarts.

Heath’s resume also includes board involvement at Wiz during the company’s journey to its eventual $32 billion acquisition by Google, giving Glow a direct line to one of the most closely watched cloud security stories in recent years.

This combination of product, engineering and security leadership appears designed to reassure buyers and investors that Glow understands the realities of enterprise deployment, not just the promise of AI.

Roi Tiger said the company’s core premise is that AI is now landing on the endpoint in a way the industry has not seen before, unlike the earlier shift from on-premises software to cloud and SaaS.

What customers is Glow targeting?

Glow says it is already working with paying customers across healthcare, retail and financial services, though it has not revealed the names or number of those customers. The company says typical deployments cover tens of thousands of employee devices spread across global organizations.

That scale suggests the startup is not targeting small teams experimenting with AI security. Instead, it is going after large enterprises that must manage thousands of laptops, developer environments and servers, often across multiple regions and compliance regimes.

Those are exactly the environments where a single bad package, unapproved AI agent or weak endpoint configuration can become a broader security issue. By focusing on control at the device level, Glow is trying to insert itself earlier into the decision chain.

Why enterprise buyers may care

Enterprise security teams are under pressure to support AI adoption without increasing their attack surface. In practice, that means they must allow employees to use new tools while still enforcing policy, verifying software provenance and reducing the risk of hidden dependencies.

Glow’s value proposition is that it can help companies keep pace with AI sprawl at the endpoint, where modern work actually happens. That is especially relevant for developers, who often install packages and tools that bypass traditional procurement workflows.

• More AI tools are being deployed directly on employee devices.
• Attackers are using generative AI to scale phishing and malware creation.
• Developer environments rely heavily on third-party packages and agents.
• Security teams need controls that act before threats are executed.

How big is the competition?

Glow is entering a mature and highly competitive market dominated by CrowdStrike, Microsoft, SentinelOne and Palo Alto Networks. Those companies already have broad endpoint security platforms and strong relationships with enterprise buyers.

That makes Glow’s challenge twofold. It has to convince customers that AI-native security is not just a feature add-on, but a meaningful new category. It also has to show that its platform can coexist with, or outperform, established endpoint products already in place.

Tiger argues that the difference lies in prevention. Existing endpoint detection and response tools are largely optimized to detect suspicious behavior once an attack is underway. Glow is trying to stop risky software and AI systems from entering the environment in the first place.

If that philosophy catches on, Glow could help define a new layer in enterprise security. If not, it may be absorbed into the broader endpoint market as another point solution chasing a fast-moving threat landscape.

What the funding tells us about the market

Glow’s fundraising is notable not just for its size, but for the speed at which the company secured a unicorn valuation. A $1.2 billion price tag before public revenue disclosure suggests venture investors believe AI-driven security problems will produce significant new spending.

It also reflects a broader pattern in cybersecurity investing. Startups that can frame themselves as indispensable infrastructure for AI adoption are commanding attention from major funds, especially if they are led by operators with proven records at top cloud and security companies.

The all-equity structure of the Series A may also matter. It implies the round was focused on ownership and growth rather than debt or other financing mechanics, and that investors want to back the company’s long-term expansion into a category that may take time to define.

For the market, the larger question is whether security budgets will shift toward AI-specific device controls quickly enough to support another layer of tooling. Enterprises are still experimenting, but the risk profile around AI-assisted attacks is already changing expectations.

Timeline of Glow’s rapid rise

Glow’s path from founding to unicorn status has been unusually fast, even by startup standards. The company was formed in 2025 and emerged from stealth in July 2026, signaling a compressed timeline that reflects both investor urgency and customer demand.

That sort of momentum is common in markets where buyers believe a new technological shift is creating a new category. In this case, Glow is wagering that AI will do for endpoint security what cloud computing did for infrastructure: force a rethink of how protection is designed and deployed.

Why the company is emphasizing prevention over detection

Glow’s biggest strategic claim is that security needs to move earlier in the workflow. The company says it should not be enough to find malicious behavior after software or an AI agent is already running on a device.

Instead, it wants to inspect the software supply chain, understand what tools are attempting to execute and block unsafe additions before they can touch the environment. That is why malicious npm packages are central to the product narrative: they represent a concrete, modern threat vector tied to application development and AI-assisted workflows.

By focusing on prevention, Glow is also appealing to companies that are overwhelmed by alert volume. Traditional security tools can generate large numbers of warnings that require human review. A system that can stop questionable software before it runs may reduce noise and help teams prioritize genuinely dangerous activity.

Still, prevention-centric tools must earn trust. Enterprises will want proof that the system does not overblock legitimate development work or create friction for employees trying to do their jobs. That operational balance will likely determine whether the category can scale.

What comes next for Glow?

Glow now has capital, a prominent investor lineup and an executive team with credibility in enterprise security. The next test is whether it can turn its AI-focused thesis into durable customer adoption and clear product differentiation.

One near-term challenge will be proving that AI-native endpoint security is more than a trend-driven slogan. Another will be demonstrating that the company can integrate into existing enterprise security stacks without adding complexity.

The broader opportunity is substantial. If AI continues to spread across employee devices, codebases and internal workflows, companies will need visibility and policy enforcement that can keep up. Glow is seeking to become one of the first vendors to make that case at scale.

For now, the startup has made a strong entrance: a high-profile stealth exit, a large Series A and a simple message that resonates with many CISOs and security teams. AI has changed the threat model, Glow argues, and endpoint security has to change with it.

Key facts at a glance

• Company: Glow
• Founded: 2025
• Headquarters: Palo Alto
• Funding: $180 million Series A
• Valuation: $1.2 billion
• Lead investors: Sequoia Capital and Cyberstarts
• Primary focus: AI-native endpoint security
• Reported customers: Healthcare, retail and financial services