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.”

DeepSee.ai raises $22.6M Series A for its AI-centric process automation platform

Published March 15, 2021

DeepSee.ai, a startup that helps enterprises use AI to automate line-of-business problems, today announced that it has raised a $22.6 million Series A funding round led by led by ForgePoint Capital. Previous investors AllegisCyber Capital and Signal Peak Ventures also participated in this round, which brings the Salt Lake City-based company’s total funding to date to $30.7 million.

The company argues that it offers enterprises a different take on process automation. The industry buzzword these days is “robotic process automation,” but DeepSee.ai argues that what it does is different. I describe its system as “knowledge process automation” (KPA). The company itself defines this as a system that “mines unstructured data, operationalizes AI-powered insights, and automates results into real-time action for the enterprise.” But the company also argues that today’s bots focus on basic task automation that doesn’t offer the kind of deeper insights that sophisticated machine learning models can bring to the table. The company also stresses that it doesn’t aim to replace knowledge workers but helps them leverage AI to turn into actionable insights the plethora of data that businesses now collect.

Image Credits: DeepSee.ai

“Executives are telling me they need business outcomes and not science projects,” writes DeepSee.ai CEO Steve Shillingford. “And today, the burgeoning frustration with most AI-centric deployments in large-scale enterprises is they look great in theory but largely fail in production. We think that’s because right now the current ‘AI approach’ lacks a holistic business context relevance. It’s unthinking, rigid and without the contextual input of subject-matter experts on the ground. We founded DeepSee to bridge the gap between powerful technology and line-of-business, with adaptable solutions that empower our customers to operationalize AI-powered automation — delivering faster, better and cheaper results for our users.”

To help businesses get started with the platform, DeepSee.ai offers three core tools. There’s DeepSee Assembler, which ingests unstructured data and gets it ready for labeling, model review and analysis. Then, DeepSee Atlas can use this data to train AI models that can understand a company’s business processes and help subject-matter experts define templates, rules and logic for automating a company’s internal processes. The third tool, DeepSee Advisor, meanwhile focuses on using text analysis to help companies better understand and evaluate their business processes.

How artificial intelligence will be used in 2021

Currently, the company’s focus is on providing these tools for insurance companies, the public sector and capital markets. In the insurance space, use cases include fraud detection, claims prediction and processing, and using large amounts of unstructured data to identify patterns in agent audits, for example.

That’s a relatively limited number of industries for a startup to operate in, but the company says it will use its new funding to accelerate product development and expand to new verticals.

“Using KPA, line-of-business executives can bridge data science and enterprise outcomes, operationalize AI/ML-powered automation at scale, and use predictive insights in real time to grow revenue, reduce cost and mitigate risk,” said Sean Cunningham, managing director of ForgePoint Capital. “As a leading cybersecurity investor, ForgePoint sees the daily security challenges around insider threat, data visibility and compliance. This investment in DeepSee accelerates the ability to reduce risk with business automation and delivers much-needed AI transparency required by customers for implementation.”