Reducing False Positives & Efficiently Scaling Security
Smart Security for the Smart World
With the explosion of connected devices in modern networks, security experts need more than basic forms of automation to secure an ever-growing amount of potentially vulnerable assets connected to their networks.
Automation in current vulnerability assessment software only follows a pre-established script where all courses of actions have already been made by security experts, with often complex and hard to maintain configurations and human intervention when a new or unplanned situation is encountered.
Scaling security for those networks requires a smarter approach.
With an intelligent autonomous system able to make choices on its own, accomplishing its objectives without human intervention and able to detect false positives, security experts can now count on an invaluable helping hand to scale security the way their network does.
Machine Learning is one of the most modern and dynamic field of research in computer science, often referred to as the holy grail of artificial intelligence. With an objective to construct algorithms that are able to learn from and make prediction on data rather than follow pre-established static program instructions, its allows the creation of software capabilities that comes close to mimicking human intelligence. Our use of deep learning techniques provides us with the ability to help security experts reduce the burden of removing false-positives in large vulnerabilities dataset for the most complex environments.
Expert Systems have the objective to emulate the decision process that a human expert of a particular domain would employ. Using a base of pre-determined rules defined through knowledge engineering and extracted in real-time, the expert system will respond properly depending on the observed environment. Our experienced security experts have devised an exhaustive set of such rules that are used at the heart of our solution, vastly improving its detection capability and streamlining the workload, conferring us the ability to cover much larger security scenarios in the most efficient way.
Named after the famous mathematician Thomas Bayes, this branch of decisional logic applies decision making using inferential statistics : it interprets probability distributions as a degree of subjective belief depending on previous information and knowledge collected. Our solution strategically uses bayesian logic at specific points in time, this allows us to maximize path coverage efficiency in the largest of networks and applications, delving deeper to expose previously unknown vulnerabilities.
Ease of use at it’s core
IT Security should be about remediation, not tool configuration. We designed our solution to be the most helpful and easy to use as possible by minimizing UX friction, using Material Design from Google that “synthesizes the classic principles of good design with the innovation and possibility of technology and science”. This allows security teams to onboard additional resources for vulnerability detection and dedicate the technical expertise for more advanced tasks.
IoT ready security that grows with you
Leaving behind the old monolithic security engines, we focused on building a modular microservices oriented platform that is able to scale laterally to cover the most diverse and stringent network requirements.
Our technology provides a cost-effective way of deploying smart security to millions of assets.
The solution was built on the shoulders of giants using well-established, renowned cloud-ready technologies, giving it the ability to scale to the Internet Of Things proportions.
Committed to the community
Not only do we actively participate in organizing the largest on-site applied security event, “NorthSec”, we’re also constantly improving our solution and publishing part of our findings for the benefit of the whole community.
Our commitment to openness doesn’t stop here, at a time where others restrict their users into walled-in solutions, we believe security products benefit from integrating and collaborating with each other, that’s why we use standardized JSON APIs and provide our customers with a documented SDK.