FortifyData, Inc., a cybersecurity firm, has developed a new approach to building a dynamic risk management model that addresses the pressing need for real-time and proactive cybersecurity measures. This invention moves beyond the traditional passive analysis techniques, offering an active and adaptive approach to managing cyber risks across networked systems.

At the heart of FortifyData’s invention is a method that involves sampling multiple organization networks and assessing their identified security features. The invention uniquely ranks these security features based on the security risk they pose, utilizing an array of logistical models, such as random forests, deep learning (AI), and decision trees. This comprehensive assessment allows for a precise ranking of features based on their impact, transforming these ranked features into categorized factors that reflect their likelihood of contributing to a breach.

A significant advancement in this method is the transformation of the logistics model from a multiplicative to an additive model, enhancing scalability and flexibility. This transformation allows for the active assessment of threats, enabling organizations to control online transactions effectively. FortifyData’s model can prevent unauthorized message alterations, incomplete transmissions, mis-routed messages, unauthorized disclosures, and even unauthorized message duplication or replay, thereby safeguarding against a broad spectrum of cyber threats.

By dividing organization networks into sample and training sets, with the training set comprising at least 70% of the data, FortifyData ensures robust and precise model training. This approach employs logistic regression to predict the likelihood of breaches, making it a potent tool for cyber risk management. Furthermore, the incorporation of environmental risk assessments, vendor risk evaluations, and compliance checks ensures that organizations have a comprehensive view of their cybersecurity landscape.

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