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In the realm of cybersecurity, it is crucial to have a systematic framework for identifying, assessing, and prioritizing cyber risks based on severity, criticality, and likelihood. However, current solutions in the market are failing to deliver on this front. Scorecards, for instance, merely inventory security issues without providing any context or focus. Endpoint tools, while important, are definitionally reactive in nature. Even leading threat intelligence solutions, while producing interesting and compelling research, often just repackage open-source reporting that lacks the ultimate ‘so-what’ for the stakeholder.
What is needed is a proactive and repeatable approach rooted in science that offers reliable cyber metrics indicative of actual risk. This is where Cybeta comes in. Our patented, quantitative cyber risk metric, Threat βeta, has been developed by data science experts and utilizes machine learning algorithms and vector quantization clustering to output predictive analytics about the likelihood of future cyber events. With Threat βeta, enterprises can gain a comprehensive understanding of their overall susceptibility or probability of experiencing a cyber risk scenario.
Threat βeta is a cutting-edge solution that provides clients with increased confidence in their ability to understand the likelihood of experiencing cyber risk scenarios. Our approach is continuously adaptive, cyber-focused, and data-driven, ensuring that our clients receive the most accurate and comprehensive information available.
Our unique threat clustering methodology utilizes machine learning to calculate cyber risk from the ground up. We analyze web statistics on discoverable public IP ports, technology vulnerability metrics, and threat actor TTPs, as well as documented exploits of networked assets and dark web mentions of technology footprints. This approach allows us to provide our clients with a comprehensive understanding of the cumulative and statistical likelihood of experiencing a negative cyber event.
We never rely on information sourced from clients and eliminate the potential for subjective input to create built-in bias. By focusing on data and attributes that matter, relevant inputs are equalized which allows for a meaningful and repeatable approach statistically vetted for reliability that can empower stakeholders to better forecast breach likelihood down to the IP-level of their organization.