Okta has been granted a patent for a system that generates network perimeters for organizations based on connection data. The system uses a machine learning model to determine the security of network zones and adjusts the model based on connection requests. The determined network perimeter can be used to implement network policies for the organization. GlobalData’s report on Okta 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 Okta, automation system authentication was a key innovation area identified from patents. Okta's grant share as of September 2023 was 52%. Grant share is based on the ratio of number of grants to total number of patents.
Determining network perimeters using machine learning for organizations
A recently granted patent (Publication Number: US11765129B2) describes a computer-implemented method for determining network perimeters using machine learning. The method involves initializing a machine learning model that receives a network zone as input and outputs a score indicating the security of the network zone in relation to an organization. The model is trained using information describing connection requests received from client devices associated with the organization, with each request originating from a network address. The parameters of the model are adjusted based on this information to improve the accuracy of predictions.
Once the model is trained, it is used to determine the network perimeter for the organization. This network perimeter is then used to implement a network policy for the organization. The method also involves determining whether a network zone is within the network perimeter based on the score generated by the model. If the score indicates that the likelihood of receiving a connection request from a malicious actor within the network zone is below a threshold, it is determined that the network zone is within the network perimeter. Conversely, if the score indicates that the likelihood is above the threshold, it is determined that the network zone is outside the network perimeter.
Based on this determination, a recommendation is sent. Subsequently, a modification to the definition of the network perimeter may be determined, and it is checked whether this modification conforms to the recommendation. If it does, the machine learning model is adjusted based on the modification to the definition of the network perimeter.
The patent also describes a non-transitory computer-readable storage medium and a computer system that implement the same method.
Overall, this patent presents a computer-implemented method that utilizes machine learning to determine network perimeters and assess the security of network zones in relation to an organization. By adjusting the machine learning model based on connection request information, the accuracy of predictions can be improved, allowing for effective implementation of network policies.
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GlobalData Patent Analytics tracks bibliographic data, legal events data, point in time patent ownerships, and backward and forward citations from global patenting offices. Textual analysis and official patent classifications are used to group patents into key thematic areas and link them to specific companies across the world’s largest industries.
