Abstract: Methods and systems are described herein for synchronizing communication records in computer networks. For example, the methods and systems may determine whether or not a first communication relates to a second and generate a recommendation that the communications relate to a single communication. In particular, the methods and systems described herein describe synchronizing communication records in computer networks based on detecting patterns in categories of metadata. For example, the methods and systems retrieve specific types of metadata and compare this metadata between communications in order to synchronize and/or deduplicate them.

Abstract: At least selectively utilizing a large language model (LLM) in generating a natural language (NL) based summary to be rendered in response to a query. In some implementations, in generating the NL based summary additional content is processed using the LLM. The additional content is in addition to query content of the query itself and, in generating the NL based summary, can be processed using the LLM and along with the query content—or even independent of the query content. Processing the additional content can, for example, mitigate occurrences of the NL based summary including inaccuracies and/or can mitigate occurrences of the NL based summary being over-specified and/or under-specified.

Abstract: At least selectively utilizing a large language model (LLM) in generating a natural language (NL) based summary to be rendered in response to a query. In some implementations, in generating the NL based summary additional content is processed using the LLM. The additional content is in addition to query content of the query itself and, in generating the NL based summary, can be processed using the LLM and along with the query content—or even independent of the query content. Processing the additional content can, for example, mitigate occurrences of the NL based summary including inaccuracies and/or can mitigate occurrences of the NL based summary being over-specified and/or under-specified.

Abstract: At least selectively utilizing a large language model (LLM) in generating a natural language (NL) based summary to be rendered in response to a query. In some implementations, in generating the NL based summary additional content is processed using the LLM. The additional content is in addition to query content of the query itself and, in generating the NL based summary, can be processed using the LLM and along with the query content—or even independent of the query content. Processing the additional content can, for example, mitigate occurrences of the NL based summary including inaccuracies and/or can mitigate occurrences of the NL based summary being over-specified and/or under-specified.

Abstract: System and methods may detect and mitigate cybersecurity threats to containers. The systems may scan an image of the container using a vulnerability scanner to generate a scan result identifying a cybersecurity vulnerability present in the image. The System may receive threat-intelligence data from a threat intelligence source comprising a first set of information regarding a plurality of cybersecurity vulnerabilities in addition to ground-truth data from a ground-truth data source comprising a second set of information regarding the plurality of cybersecurity vulnerabilities. Ground truth data and threat-intelligence data may be aggregated to generate aggregated data identifying an exploit related to the cybersecurity vulnerability. The aggregated data may be aligned with the scan result to identify the exploit for the cybersecurity vulnerability.