Lacework’s patent involves a method for detecting anomalous behavior of a user device by analyzing historical and current activity to determine deviations from normal behavior. This user-specific data-driven network security approach triggers an approval workflow for suspicious network requests, enhancing overall security measures. GlobalData’s report on Lacework gives a 360-degree view of the company including its patenting strategy. Buy the report here.
According to GlobalData’s company profile on Lacework, Network threat detection was a key innovation area identified from patents. Lacework'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.
User-specific data-driven network security method

A recently granted patent (Publication Number: US11991198B1) discloses a method for user-specific data-driven network security. The method involves generating a trained model based on a user's historical activity to determine if a network request deviates from normal behavior. Upon receiving the network request from the user's device, the system analyzes it against the historical data and the trained model to identify any anomalies. If a deviation is detected, an approval workflow is initiated, consisting of user-completable steps that, when finished, allow the network request to proceed.
Furthermore, the patent includes provisions for periodically retraining the model based on workflow completions or additional historical user activity. The approval workflow can be customized to include self-approval or third-party approval processes, with the ability to select from multiple workflows. The system also determines whether to allow the network request based on the completion of the approval workflow or if the request aligns with the user's normal activity. A computer program product on a non-transitory computer-readable medium is also described, implementing the same steps for user-specific data-driven network security, including model training, anomaly detection, and approval workflow initiation based on deviations from normal user behavior. This innovative approach aims to enhance network security by leveraging user-specific data and tailored approval workflows to mitigate potential risks effectively.
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