Machine Learning (ML) has revolutionized the way companies evaluate and prioritize patching of security vulnerabilities. The Exploit Prediction Scoring System (EPSS), developed by a team of researchers, is an ML-based tool that improves prediction accuracy by 82% compared to previous versions. This system can help organizations identify which vulnerabilities are most likely to be exploited to better protect their systems from malicious actors.
The EPSS uses multiple data sources, such as vulnerability databases, threat intelligence feeds, and open-source code repositories, to generate a score for each vulnerability based on its exploitability potential. This scoring system helps companies quickly assess the risk posed by different types of threats so they can determine which ones need immediate attention or remediation measures taken against them first. Additionally, it allows them to prioritize patches more effectively according to their risk level rather than relying solely on manual processes or guesswork when deciding what needs fixing first.
Overall, ML-driven tools like EPSS provide invaluable assistance for organizations looking for ways to improve their cyber security posture without having to dedicate large amounts of resources towards manually evaluating every single vulnerability discovered within their networks or applications used daily basis. By using this type of predictive technology, businesses will be able to make informed decisions about how best to address any potential risks before they become major issues down line.
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