Virginia Patent of the Month – September 2024
Trackd has developed a cutting-edge solution that tackles the complex and time-consuming process of vulnerability lifecycle management in cyber security. The system integrates advanced machine learning (ML) models with endpoint telemetry data to predict the risk impact of applying vulnerability remediations, such as security patches or software updates, to endpoint devices. This predictive capability allows organizations to automate and streamline the remediation process while reducing the uncertainty and potential risks associated with patch deployment.
The system features several key components that work together to create an intelligent and responsive security environment. At the heart of the system is a security and vulnerability analysis processor, which communicates with endpoint devices to collect and analyze telemetry data. This data, which includes operating status and process information, is used by ML models to generate predictions about the potential impact of applying remediations to specific endpoints.
One of the most powerful aspects of this system is its ability to continuously learn and adapt. The machine learning models are trained using security data from a centralized database, which includes information about vulnerabilities, endpoint profiles, and patching history. As new vulnerability data is ingested, the contextualization engine correlates it with existing data, ensuring that endpoints remain accurately assessed and protected. This dynamic system ensures that decisions about remediation are based on up-to-date, precise information, minimizing the risk of disruptions while addressing security flaws.
Trackd’s innovative approach not only automates the vulnerability remediation process but also provides a deeper understanding of how vulnerabilities interact with endpoint configurations. By leveraging the power of machine learning, Trackd is helping organizations stay ahead of cyber threats with faster, more reliable, and data-driven decisions.
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