Cybersecurity startup Panaseer raises $26.5M Series B led by AllegisCyber Capital

Published May 11, 2021

Panaseer, which takes a data science approach to cybersecurity, has raised $26.5 million in a Series B funding led by AllegisCyber Capital. Existing investors, including Evolution Equity Partners, Notion Capital, AlbionVC, Cisco Investments and Paladin Capital Group, as well as new investor National Grid Partners, also participated. Panaseer has now raised $43 million to date.

Panaseer’s special sauce and sales pitch amount to what it calls “Continuous Controls Monitoring” (CCM). In plainer English that means correlating a great deal of data from all available security tools to check assets, control gaps, you name it.

As a result, the company says it can identify zero-day and other exposures faster, or exposure to, say, FireEye or SolarWinds vulnerabilities.

Jonathan Gill, CEO, Panaseer said: “Most enterprises have the tools and capability to theoretically prevent a breach from occurring. However, one of the key reasons that breaches occur is that there is no technology to monitor and react to failed controls. CCM continuously validates and measures levels of protection and provides notifications of failures. Ultimately, CCM enables these failures to be fixed before they become security incidents.”

Speaking to me on a call he added: “The investment, allows us to scale our organization to meet those demands of customers with a team of people to implement the platform and help them get tremendous value and to evolve the product. To add more and more capability to that technology to support more and more use cases. So they’re the two main directions, and there’s a market we think of tens of thousands of organizations of a certain size, who are regulated or they have assets worth protecting and a level of complexity that makes it difficult to solve the problem themselves. And our Advisory Board and the customers I’ve spoken with think maybe there are barely 20 companies in the world who can solve this problem. And everybody else gets stuck on the fact that it’s a really difficult data science problem to solve. So we want to scale that and take that to more organizations.”

How to respond to a data breach

And why did they pick these investors: “I think we picked them and they picked us, we’ve been on that journey together. It takes months to find the best combination. The dollars are all the same when it comes to investors, but I think they can help improve as an organization and grow just like the existing investors do. They give us access and reach into parts of the market and help make us better as organizations as well.”

Bob Ackerman, founder and managing director of AllegisCyber Capital, and co-founder of DataTribe said: “The emergence of Continuous Controls Monitoring as a new cybersecurity category demonstrates a ‘coming of age’ for cybersecurity. Cyber is the existential threat to the global digital economy. All levels of the enterprise, from the CISO, to Chief Risk Officer, to the Board of Directors are demanding comprehensive visibility, transparency and hard metrics to assess cyber situational awareness.”

AI-tool maker Seldon raises £7.1M Series A from AlbionVC and Cambridge Innovation Capital

Published November 18, 2020

Seldon is a U.K. startup that specializes in the rarified world of development tools to optimize machine learning. What does this mean? Well, dear reader, it means that the “AI” that companies are so fond of trumpeting does actually end up working.

It has now raised a £7.1 million Series A round co-led by AlbionVC and Cambridge Innovation Capital . The round also includes significant participation from existing investors Amadeus Capital Partners and Global Brain, with follow-on investment from other existing shareholders. The £7.1 million funding will be used to accelerate R&D and drive commercial expansion, take Seldon Deploy — a new enterprise solution — to market and double the size of the team over the next 18 months.

More accurately, Seldon is a cloud-agnostic machine learning (ML) deployment specialist which works in partnership with industry leaders such as Google, Red Hat, IBM and Amazon Web Services.

Key to its success is that its open-source project Seldon Core has more than 700,000 models deployed to date, drastically reducing friction for users deploying ML models. The startup says its customers are getting productivity gains of as much as 92% as a result of utilizing Seldon’s product portfolio.

Alex Housley, CEO and founder of Seldon speaking to TechCrunch explained that companies are using machine learning across thousands of use cases today, “but the model actually only generates real value when it’s actually running inside a real-world application.”

“So what we’ve seen emerge over these last few years are companies that specialize in specific parts of the machine learning pipeline, such as training version control features. And in our case we’re focusing on deployment. So what this means is that organizations can now build a fully bespoke AI platform that suits their needs, so they can gain a competitive advantage,” he said.

In addition, he said Seldon’s open-source model means that companies are not locked-in: “They want to avoid locking as well they want to use tools from various different vendors. So this kind of intersection between machine learning, DevOps and cloud-native tooling is really accelerating a lot of innovation across enterprise and also within startups and growth-stage companies.”

Nadine Torbey, an investor at AlbionVC, added: “Seldon is at the forefront of the next wave of tech innovation, and the leadership team are true visionaries. Seldon has been able to build an impressive open-source community and add immediate productivity value to some of the world’s leading companies.”

Vin Lingathoti, partner at Cambridge Innovation Capital, said: “Machine learning has rapidly shifted from a nice-to-have to a must-have for enterprises across all industries. Seldon’s open-source platform operationalizes ML model development and accelerates the time-to-market by eliminating the pain points involved in developing, deploying and monitoring machine learning models at scale.”