Threat βeta™

Data-Driven Cyber Metrics Forecasting Statistical Likelihood of Risk

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Cybeta Solutions

Generating Statistically Reliable Analytics on Future Cyber Events

When trying to understand overall enterprise susceptibility or probability of a cyber risk scenario, incumbent solutions are failing to deliver a systematic framework for identifying, assessing, and prioritizing cyber risks based on severity, criticality, and likelihood.

Whether scorecards which typically just inventory security issues without context or focus, or endpoint tools which are important but nonetheless definitionally reactive in nature, or even leading threat intelligence solutions, which can produce interesting and compelling research, yet often times just repackages open-source reporting that lacks the ultimate ‘so-what’ for the stakeholder, it is clear that what has been missing is a proactive and repeatable approach rooted in science that offers reliable cyber metrics indicative of actual risk.  

Cybeta answers this problem through Threat βeta, a patented, quantitative cyber risk metric developed by data science experts that outputs predictive analytics about the likelihood of future cyber events using machine learning algorithms and vector quantization clustering.

With Threat βeta, customers can:

Baseline Enterprise Cyber Risk or at Portfolio Level
Baseline Against Competitors, Peers, Supply Chain, and Industry
Reduce Likelihood of Cyber Risk Scenarios
Inform and Maximize Security Spend & ROI
Prioritize Remediation

Adapting at The Speed of Threats

Threat βeta is designed to offer higher confidence values to clients looking to grasp the cumulative and statistical likelihood, on average, of experiencing cyber risk scenarios by being continuously adaptive, cyber-focused, and data-driven.

Our unique threat clustering methodology leverages machine learning and calculates cyber risk using a bottom-up approach: we examine web statistics on discoverable public IP ports, technology vulnerability metrics and threat actor TTPs, documented exploits of networked assets, and dark web mentions of technology footprints. 

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.