Tenable. has been granted a patent for a method that generates a threat score prediction model to assess software vulnerabilities. The model incorporates data from public media, exploit databases, third-party threat intelligence, and enterprise network behavior to evaluate the likelihood of exploitation. GlobalData’s report on Tenable gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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According to GlobalData’s company profile on Tenable, was a key innovation area identified from patents. Tenable's grant share as of June 2024 was 60%. Grant share is based on the ratio of number of grants to total number of patents.
Threat score prediction model for software vulnerabilities
The patent US12019757B2 outlines a method for training a model to assess threat scores associated with software vulnerabilities. This method involves obtaining training data related to a specific set of vulnerabilities, which includes various factors such as the presence of exploits in public databases, historical threat intelligence from third-party sources, and media coverage of the vulnerabilities. The model generated from this data is designed to predict the likelihood that a candidate vulnerability will be targeted for exploitation or successfully exploited within a defined timeframe. Additional training data may encompass the complexity of exploit development, customer perceptions of the vulnerabilities, and remediation metrics, which further refine the threat score predictions.
Moreover, the patent details the inclusion of secondary training data that characterizes the age of vulnerabilities, the prevalence of exploits, and the behavior of enterprise networks in relation to these vulnerabilities. The model can also incorporate inter-customer metrics, allowing for a more nuanced understanding of how different customers perceive and respond to vulnerabilities. The apparatus described in the patent consists of a memory and processor that facilitate the collection and analysis of this data, ultimately generating a threat score that reflects the potential risk associated with software vulnerabilities. This comprehensive approach aims to enhance the accuracy of threat assessments in cybersecurity contexts.
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