Abstract: A system and method for generating multi-source cybersecurity policies is presented. The method includes receiving a plurality of cybersecurity policies, wherein a first cybersecurity policy is stored in a first data format, and a second cybersecurity policy is stored in a second data format; generating a normalized cybersecurity policy based on each received cybersecurity policy, including a normalized first cybersecurity policy and a normalized second cybersecurity policy; generating a first generated cybersecurity policy based on the normalized first cybersecurity policy, utilizing the second data format; generating a second generated cybersecurity policy based on the normalized second cybersecurity policy, utilizing the first data format; and applying each of the generated cybersecurity policies on a respective cybersecurity platform.

Abstract: Techniques for data classification. A method includes sampling a dataset into first and second samples. Each first sample is a numerical value, and each second sample is a string of characters. A truth table is applied to the first samples from a dataset. The truth table includes multiple first columns, each of which accepts an input value determined for each of the first samples, and a second column which outputs first scores representing likelihoods for respective classifications. Classifiers are applied to features extracted from the second samples, where each classifier is a machine learning model trained to output a second score representing a likelihood for a respective classification for each second sample. Classifications are determined based on the first and second scores. The classifications include a classification for each first sample determined based on the first scores and a classification for each second sample determined based on the second scores.

Abstract: Techniques for training and using machine learning models for resource-level classification. A method for training includes refining outputs of a language model by providing a prompt and a set of sample resources to the language model over a series of iterations. Accuracies for the classifications output by the language model at each iteration are determined based on semantic similarity between those classifications and corresponding reference classifications for the sample resources. The language model is applied to data of a set of training resources when the outputs of the language model have been refined, in order to output a set of classifications for the set of training resources. Training data is labeled based on the set of classifications output by the language model. A classifier machine learning model is trained via supervised machine learning using the set of labeled training data in order to produce a trained classifier machine learning model.

Abstract: Techniques for data classification using clustering. A method includes replacing a plurality of portions of metadata for a plurality of data objects with a plurality of replacement characters in order to generate a plurality of replaced strings; clustering the plurality of data objects into a plurality of clusters based on commonalities between the plurality of replaced strings of data objects of the plurality of data objects; classifying a subset of the data objects in each cluster into at least one class; and aggregating classes within at least one cluster of the plurality of clusters, wherein aggregating classes within each of the at least one cluster includes applying the at least one class for the subset of the data objects in each cluster to each other data object within the cluster.

Abstract: Techniques for data classification using clustering. A method includes replacing a plurality of portions of metadata for a plurality of data objects with a plurality of replacement characters in order to generate a plurality of replaced strings; clustering the plurality of data objects into a plurality of clusters based on commonalities between the plurality of replaced strings of data objects of the plurality of data objects; classifying a subset of the data objects in each cluster into at least one class; and aggregating classes within at least one cluster of the plurality of clusters, wherein aggregating classes within each of the at least one cluster includes applying the at least one class for the subset of the data objects in each cluster to each other data object within the cluster.

Abstract: Techniques for data classification using clustering. A method includes replacing a plurality of portions of metadata for a plurality of data objects with a plurality of replacement characters in order to generate a plurality of replaced strings; clustering the plurality of data objects into a plurality of clusters based on commonalities between the plurality of replaced strings of data objects of the plurality of data objects; classifying a subset of the data objects in each cluster into at least one class; and aggregating classes within at least one cluster of the plurality of clusters, wherein aggregating classes within each of the at least one cluster includes applying the at least one class for the subset of the data objects in each cluster to each other data object within the cluster.

Abstract: A system and method for data discovery. A method includes performing a scan of a plurality of snapshots, each snapshot corresponding to a respective disk of a plurality of disks; identifying a plurality of data store files in the plurality of disks based on file metadata found during the scan; and detecting at least one data store based on the identified plurality of data store files, wherein each of the at least one data store is in a disk of the plurality of disks including one of the plurality of data store files.

Abstract: A system and method for data discovery. A method includes performing a scan of a plurality of snapshots, each snapshot corresponding to a respective disk of a plurality of disks; identifying a plurality of data store files in the plurality of disks based on file metadata found during the scan; and detecting at least one data store based on the identified plurality of data store files, wherein each of the at least one data store is in a disk of the plurality of disks including one of the plurality of data store files.

Abstract: Techniques for data classification. A method includes sampling a dataset into first and second samples. Each first sample is a numerical value, and each second sample is a string of characters. A truth table is applied to the first samples from a dataset. The truth table includes multiple first columns, each of which accepts an input value determined for each of the first samples, and a second column which outputs first scores representing likelihoods for respective classifications. Classifiers are applied to features extracted from the second samples, where each classifier is a machine learning model trained to output a second score representing a likelihood for a respective classification for each second sample. Classifications are determined based on the first and second scores. The classifications include a classification for each first sample determined based on the first scores and a classification for each second sample determined based on the second scores.

Abstract: A system and method for data discovery. A method includes performing a scan of a plurality of snapshots, each snapshot corresponding to a respective disk of a plurality of disks; identifying a plurality of data store files in the plurality of disks based on file metadata found during the scan; and detecting at least one data store based on the identified plurality of data store files, wherein each of the at least one data store is in a disk of the plurality of disks including one of the plurality of data store files.

Abstract: A system and method for discovering data store locations. A method includes reading, for each disk of a plurality of disks deployed in a cloud environment, only a portion of a snapshot of the disk accessed via a cloud provider tool, wherein the portion of the snapshot of each disk accessed via the cloud provider tool includes file system metadata of a file system of the disk, wherein the cloud provider tool is configured to provide direct access to data from each of the plurality of disks; analyzing the portion of the snapshot of each disk of the plurality of disks to determine whether each disk contains a data store; and identifying, based on the analysis, at least one data store in the cloud environment.