Lacework has been granted a patent for a natural language interface that enhances anomaly detection in cloud deployments. The technology involves receiving and interpreting natural language inputs related to cloud activity, generating queries based on anomaly detection data, and providing responses accordingly. 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 April 2024 was 58%. Grant share is based on the ratio of number of grants to total number of patents.
Natural language interface for anomaly detection framework in cloud deployment
A recently granted patent (Publication Number: US11973784B1) discloses a method and computer program product for providing a natural language interface for an anomaly detection framework in a cloud deployment setting. The method involves receiving a natural language input related to the monitored cloud deployment, generating a query by disambiguating the input based on data describing the cloud deployment's activity, and providing a response based on the query. The disambiguation process can also consider data related to the anomaly detection framework itself, customer-associated cloud deployments, expert queries, user interactions, anomalies identified, and user-provided clarifications.
Furthermore, the method includes features such as requesting clarification for the input, storing data for query generation criteria, seeking confirmation to use alternatives for query generation, and training models based on historical activity data. The computer program product, configured on a non-transitory computer-readable medium, follows a similar process of receiving natural language input, generating queries based on activity data from the anomaly detection framework, and providing responses. It also includes functionalities like considering data from other customer deployments, expert queries, user interactions, asset states, user-provided clarifications, seeking clarification, storing query criteria data, using alternatives for query generation, and training models based on historical activity data and previous inputs. These innovations aim to enhance the efficiency and accuracy of anomaly detection in cloud deployments through a user-friendly natural language interface.
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