Proofpoint had three patents in future of work during Q1 2024. Proofpoint Inc’s patents filed in Q1 2024 focus on cybersecurity training through dynamically generating simulated attack messages for user annotation and personalized training modules. Additionally, the patents cover dynamic message analysis using machine learning to monitor messaging servers, identify bi-directional messaging traffic, select external domains for conversation detection, and execute enhanced protection actions. GlobalData’s report on Proofpoint gives a 360-degree view of the company including its patenting strategy. Buy the report here.
Proofpoint grant share with future of work as a theme is 33% in Q1 2024. Grant share is based on the ratio of number of grants to total number of patents.
Recent Patents
Application: Prompting users to annotate simulated phishing emails in cybersecurity training (Patent ID: US20240078924A1)
Proofpoint Inc. has developed a system for generating simulated attack messages for cybersecurity training purposes. The system involves a computing platform that creates and sends these simulated attack messages to enterprise user devices, allowing users to interactively annotate selected elements of the messages. Based on these annotations, the system identifies specific training areas for each user, generates customized training modules, and sends them to the user devices for display. The training modules are designed to allow users to interactively complete the training based on their identified areas of improvement.
The system includes features such as calculating user performance scores, dynamically adapting training modules based on user selections, and updating machine learning models used in generating the simulated attack messages. Users can mark elements of the simulated attack messages as suspicious or indicate if they fell for the simulated attack. Additionally, the system allows for the aggregation of user selections from multiple devices and the transmission of this data to an administrator computing device. By analyzing the frequency with which elements are correctly selected by users, the system can assign adjusted score weightings to elements and tailor training modules to address specific user needs effectively.
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