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Security: SentinelOne raises $120M for its fully-autonomous, AI-based endpoint security solution

Security:

Endpoint security — the branch of cybersecurity that focuses on data coming in from laptops, phones, and other devices connected to a network — is an $8 billion dollar market that, due to the onslaught of network breaches, is growing fast. To underscore that demand, one of the bigger startups in the space is announcing a sizeable funding round.

SentinelOne, which provides real-time endpoint protection on laptops, phones, containers, cloud services and most recently IoT devices on a network through a completely autonomous, AI-based platform, has raised $120 million in a Series D round — money that it will be using to continue expanding its current business as well as forge into new areas such as building more tools to automatically detect and patch software running on those endpoints, to keep them as secure as possible.

The funding was led by Insight Partners, with Samsung Venture Investment Corporation, NextEquity participating, alongside all of the company’s existing investors, which include the likes of Third Point Ventures, Redpoint Ventures, Data Collective, Sound Ventures and Ashton Kutcher, Tiger Global, Granite Hill and more.

SentinelOne is not disclosing its valuation with this round, but CEO and co-founder Tomer Weingarten confirmed it was up compared to its previous funding events. SentinelOne has now raised just shy of $230 million, and PitchBook notes that in its last round, it was valued at $210 post-money.

That would imply that this round values SentinelOne at more than $330 million, and we understand that it’s more like $500 million:

“We are one of the youngest companies working in endpoint security, but we also have well over 2,000 customers and 300% growth year-on-year,” Weingarten said. And working in the area of software-as-a-service with a fully-automated solution that doesn’t require humans to run any aspect of it, he added, “means we have high margins.”

The rise in cyberattacks resulting from malicious hackers exploiting human errors — such as clicking on phishing links; or bringing in and using devices from outside the network running software that might not have its security patches up to date — has resulted in a stronger focus on endpoint security and the companies that provide it.

Indeed, SentinelOne is not alone. Crowdstrike, another large startup in the same space as SentinelOne, is now looking at a market cap of at least $4 billion when it goes public. Carbon Black, which went public last year, is valued at just above $1 billion. Another competitor, Cylance, was snapped up by BlackBerry for $1.5 billion.

Weingarten — who cofounded the company with Almog Cohen (CTO) — says that SentinelOne differs from its competitors in the field because of its focus on being fully autonomous.

“We’re able to digest massive amounts of data and run machine learning to detect any type of anomaly in an automated manner,” he said, describing Crowdstrike as “tech augmented by services.” That’s not to say SentinelOne is completely without human options (options being the key word; they’re not required): it offers its own managed services under the brand name of Vigilance and works with system integrator partners to sell its products to enterprises.

There is another recurring issue with endpoint security solutions, which is that they are known to throw up a lot of false positives — items that are not recognized by the system that subsequently get blocked, which turn out actually to be safe. Weingarten admits that this is a by-product of all these systems, including SentinelOne’s.

“It’s a result of opting to use a heuristic rather than deterministic model,” he said, “but there is no other way to deal with anomalies and unknowns without heuristics, but yes with that comes false positives.” He pointed out that the company’s focus on machine learning as the basis of its platform helps it to more comprehensively ferret these out and make deductions on what might not otherwise have proper representation in its models. Working for a pilot period at each client also helps inform t

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