Okta has patented a system that uses machine learning to generate network perimeters for organizations based on connection data. The system adjusts parameters of the model to improve accuracy, determining network security levels and recommending actions to network administrators for enhanced security measures. GlobalData’s report on Okta gives a 360-degree view of the company including its patenting strategy. Buy the report here.

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 May 2024 was 34%. Grant share is based on the ratio of number of grants to total number of patents.

Machine learning based network perimeter generation and policy implementation

Source: United States Patent and Trademark Office (USPTO). Credit: Okta Inc

A recently granted patent (Publication Number: US11991148B2) outlines a method for enhancing network security through the use of machine learning models. The method involves receiving connection data from client devices on a network, training a machine learning model to output scores for network zones, inputting a network zone to obtain a security score, displaying data to a network administrator, receiving approval for recommendations, and modifying network operations based on the security score post-approval. The network zones can include various parameters like IP addresses, geographical locations, and autonomous system numbers. The machine learning model, which can be a neural network, is continuously updated with new connection data to adapt to evolving network threats.

Furthermore, the patent also covers a non-transitory computer-readable storage medium and a device implementing the described method. The storage medium stores instructions for receiving connection data, training the machine learning model, inputting network zones, displaying data to network administrators, and modifying network operations based on security scores. The device, equipped with at least one processor and storage medium, executes the logic for enhancing network security. It includes features like re-training the machine learning model with updated data and automatically adjusting network policies, such as perimeter boundaries, based on the model's outputs. This innovative approach leverages machine learning to proactively assess and improve network security, providing network administrators with valuable insights and recommendations to safeguard their networks effectively.

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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.