Lacework. has been granted a patent for a method that involves generating and augmenting a logical graph through clustering operations on log data from multiple machines. The process includes an initial clustering and a subsequent reclustering, both adhering to specific relationship requirements of an underlying model. GlobalData’s report on Lacework 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 Lacework, Network threat detection was a key innovation area identified from patents. Lacework's grant share as of June 2024 was 63%. Grant share is based on the ratio of number of grants to total number of patents.
Clustering log data to generate and update logical graphs
The granted patent US12032634B1 outlines a method and system for generating and augmenting a logical graph based on log data from multiple machines. The process begins with a clustering operation that creates an initial logical graph, which includes a set of nodes representing the machines and edges that depict their communication. This initial clustering is performed using a first clustering criterion, which is influenced by machine-to-machine communications. Subsequently, the logical graph is updated through a reclustering operation that employs a different second clustering criterion. This operation replaces the initial set of nodes with a new set while preserving the underlying model's relationship requirements and the communication edges between the machines.
Additionally, the patent specifies that the reclustering can utilize user-provided tags and may generate machine types from the resulting clusters. The second clustering criteria can include various factors such as pod communication information, user information, or other attributes that may not have been present in the initial graph. The system is designed to automatically create the logical graph and respond to user interactions, with the capability to generate alerts upon changes to the graph. The described method and system aim to enhance the understanding of machine interactions and improve data analysis through dynamic graph representation.
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