Ghost Security reinvents application security with unsupervised machine learning

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Software applications are at the heart of organizations of all sizes in all industries. Using APIs and microservices creates an ecosystem between users and the information they need. Because of this, there has been an exponential expansion in the development and use of applications and APIs, often leaving them untraceable and unsecured, according to Ghost Security, an application security company.

The industry has debated how to address the security risks that cloud applications face. Several product subcategories attempt to support this goal, from Cloud Security Posture Management (CSPM) to Identity Access Management (IAM), to Web Application Firewall (WAF), data loss prevention (DAP), runtime protection tools, static analysis, and dynamic analysis.

However, despite all these point products, application compromises are on the rise, the company said.

Coming to AI with Unsupervised Machine Learning

Ghost Security, which emerged from stealth mode today, says it's taking a different approach and using machine learning (ML) as a core component of its platform. The technology allows security professionals to profile normal and abnormal behavior and detect when something abnormal is happening. "The great thing is that you have the ability to detect attacks that no one has seen before," Ghost co-founder and CEO Greg Martin told VentureBeat.

The company says its platform will help technology leaders continue rapid application development without disrupting existing processes, while providing detection and response teams with comprehensive, automated application protection.

"We're trying to innovate to create the defense not just for today's applications, but for the next ten or twenty years," Martin said. "In practice, that means using technology that wasn't available 10 or 12 years ago," such as machine learning, artificial intelligence (AI), and cloud horizontal scale systems.

According to Martin, many application security products use supervised machine learning, where algorithms are trained using good and bad data so the system understands what to look for. But Ghost uses an unsupervised machine learning approach, "where you don't have to feed it data; it's learning in a different way," he explained.

Another differentiator is “we design our software to be compatible with whatever [cloud provider] the customer uses,” Martin said. "So if [they use] Google or Amazon Web Services or Microsoft Azure - or something totally different - we'll create compatibility for each customer."

This includes customers who operate on-premises data centers, Martin added.

A better approach is needed to secure assets

"What's exciting about the Ghost Platform is that it removes the complex and invasive processes needed to protect applications and APIs, making this kind of technology more accessible to organizations around the world," Florian Leibert, general partner and co-founder of 468. Capital, said in a statement. "They're creating a solution that scales without affecting productivity and harnesses the power of machine learning to identify unknown vulnerabilities and stop more threats."

Ghost Security is backed by a combined $15 million investment from 468 Capital, DNX Ventures and Munich Re Ventures. Announcing the funding, the company said it would use this influx of capital to continue to focus on building "a world-class team with the experience and passion to develop disruptive technologies." .

"The soaring adoption of applications, APIs, and microservices represents great growth potential for enterprises, but also introduces many new attack surfaces," said Hiro Rio Maeda, managing partner at DNX Ventures, in a statement. "A better approach to securing these assets is needed, and Ghost is well positioned to meet this challenge."

Ghost...