Abstract: Data moving micro-services are deployed to a public cloud and a cloud vault target (CVT). A first request is issued to the public cloud for a first snapshot of data belonging to a customer tenant. The first snapshot is accessed to write the data to a first cloud volume in the public cloud. Data of the first cloud volume is vaulted to the CVT via the micro-services. The first cloud volume is maintained in the public cloud. A second request is issued to the public cloud for a second snapshot of the data belonging to the customer tenant. The second snapshot is accessed to write the data to a second cloud volume in the public cloud. Data of the second cloud volume is compared against data of the first cloud volume to identify redundant data. Data of the second cloud volume that is not redundant is vaulted to the CVT.

Abstract: In one example, a method includes receiving, at a cloud storage site, chunks that each take the form of a hash of a combination that includes two or more salts and a file object, and one of the salts is a retention salt shared by the chunks, monitoring a time period associated with the retention salt, when the time period has expired, removing the chunks that include the retention salt, and depositing the removed chunks in a deleted items cloud store.

Abstract: In one example, a method includes receiving, at a cloud storage site, chunks that each take the form of a hash of a combination that includes two or more salts and a file object, and one of the salts is a retention salt shared by the chunks, monitoring a time period associated with the retention salt, when the time period has expired, removing the chunks that include the retention salt, and depositing the removed chunks in a deleted items cloud store.

Abstract: In one example, a method includes receiving, at a cloud storage site, chunks that each take the form of a hash of a combination that includes two or more salts and a file object, and one of the salts is a retention salt shared by the chunks, monitoring a time period associated with the retention salt, when the time period has expired, removing the chunks that include the retention salt, and depositing the removed chunks in a deleted items cloud store.

Abstract: Data moving micro-services are deployed to a public cloud and a cloud vault target (CVT). A first request is issued to the public cloud for a first snapshot of data belonging to a customer tenant. The first snapshot is accessed to write the data to a first cloud volume in the public cloud. Data of the first cloud volume is vaulted to the CVT via the micro-services. The first cloud volume is maintained in the public cloud. A second request is issued to the public cloud for a second snapshot of the data belonging to the customer tenant. The second snapshot is accessed to write the data to a second cloud volume in the public cloud. Data of the second cloud volume is compared against data of the first cloud volume to identify redundant data. Data of the second cloud volume that is not redundant is vaulted to the CVT.

Abstract: A public cloud is owned by a first entity and a cloud vault target is owned by a second entity, different from the first entity. A data moving micro-service is deployed to the public cloud and another data moving micro-service is deployed to the cloud vault target. A request is issued to the public cloud for a snapshot of data to vault to the cloud vault target. The snapshot is accessed to write the data to a cloud volume in the public cloud. The data moving micro-service at the public cloud and the other data moving micro-service at the cloud vault target coordinate to read the data in the cloud volume, and deduplicate and replicate the data in the cloud volume from the public cloud to the cloud vault target.

Abstract: Systems and methods for determining similarity between sets of objects are disclosed. A set of hashes are generated for a set of objects. A similarity vector is generated for the set of hashes. The similarity vector is a compact representation of the set of hashes and of the corresponding set of objects. The similarity of the set of objects is determined by comparing the similarity vector of the set of objects with other similarity vectors. In a data protection system, the set of objects can be placed with the node or system that stores objects that are most similar to the set of objects being placed.

Abstract: In one example, a method includes receiving, at a cloud storage site, chunks that each take the form of a hash of a combination that includes two or more salts and a file object, and one of the salts is a retention salt shared by the chunks, monitoring a time period associated with the retention salt, when the time period has expired, removing the chunks that include the retention salt, and depositing the removed chunks in a deleted items cloud store.

Abstract: In one example, a method includes receiving, at a cloud storage site, chunks that each take the form of a hash of a combination that includes two or more salts and a file object, and one of the salts is a retention salt shared by the chunks, monitoring a time period associated with the retention salt, when the time period has expired, removing the chunks that include the retention salt, and depositing the removed chunks in a deleted items cloud store.

Abstract: In one example, a method includes receiving, at a cloud storage site, chunks that each take the form of a hash of a combination that includes two or more salts and a file object, and one of the salts is a retention salt shared by the chunks, monitoring a time period associated with the retention salt, when the time period has expired, removing the chunks that include the retention salt, and depositing the removed chunks in a deleted items cloud store.

Abstract: In one example, a method includes receiving, at a cloud storage site, chunks that each take the form of a hash of a combination that includes two or more salts and a file object, and one of the salts is a retention salt shared by the chunks, monitoring a time period associated with the retention salt, when the time period has expired, removing the chunks that include the retention salt, and depositing the removed chunks in a deleted items cloud store.

Abstract: Applying machine-learning to optimize the operational efficiency of data backup systems is described. A machine-learning system creates a training set of multiple features for each of multiple historical data backup jobs. The machine-learning system trains a prediction model to predict, based on the training set, the probabilities that the corresponding historical data backup jobs failed during the next historical data backup window. The machine-learning system creates an operational set of multiple features for each of multiple scheduled data backup jobs. The trained prediction model predicts, based on the operational set, the probabilities that the corresponding scheduled data backup jobs will fail during the next scheduled data backup window. The predicted probability that a scheduled data backup job will fail during the next scheduled data backup window is output, thereby enabling an operator to remediate the scheduled data backup job prior to the next scheduled data backup window.

Abstract: A system and method for managing data includes identifying, in response to a storage request from a tenant system, a first data protection pool based on a data classification analysis performed on data associated with the storage request and initiating storage of data associated with the storage request in a first storage system associated with the first data protection pool. A pattern matching model and data sampled from the tenant system may be used to identify data characteristics, which may include data type, data retention, data sensitivity, and data location. At least some data characteristics may be obtained using a plugin to a tenant system on which the data associated with the storage request is stored.

Abstract: A public cloud is owned by a first entity and a cloud vault target is owned by a second entity, different from the first entity. A data moving micro-service is deployed to the public cloud and another data moving micro-service is deployed to the cloud vault target. A request is issued to the public cloud for a snapshot of data to vault to the cloud vault target. The snapshot is accessed to write the data to a cloud volume in the public cloud. The data moving micro-service at the public cloud and the other data moving micro-service at the cloud vault target coordinate to read the data in the cloud volume, and deduplicate and replicate the data in the cloud volume from the public cloud to the cloud vault target.

Abstract: The implementation of an automated agent for data copy verification. Specifically, the implementation entails the execution of an intelligent, machine-learning based method and system for determining the integrity of data copies (i.e., for identifying whether data copies of a same data set have been impacted by malicious activities). Upon determining that data integrity is likely compromised, one or more corrective actions may be triggered. These actions may mitigate the spread of corruption and/or infection.

Abstract: The implementation of an automated agent for data copies verification. Specifically, the implementation entails the execution of an intelligent, machine-learning based method and system for determining the integrity of data copies (i.e., for identifying whether data copies of a same data set have been impacted by malicious activities).

Abstract: In one example, a method for managing data includes detecting, in a shared multi-tenant deduplication system, a data stream that is specific to a particular user and that includes one or more user objects each having one or more data chunks. The data associated with the user is salted by associating a user-specific salt with the data chunks of the one or more user objects to form user-specific combinations of the form ((user-specific salt)+(user data chunk)). Finally, the user data is processed using the user-specific salt.

Abstract: A method for managing data includes identifying, in response to a storage request from a tenant system, a first data protection pool based on a data classification analysis and initiating storage of data associated with the storage request in a first storage system associated with the first data protection pool.

Abstract: Input/output (I/O) inspection methods and systems are disclosed to detect and defend against cybersecurity threats. In one example, a method includes intercepting input/output (I/O) operations including I/O write operations for a storage system. Segments of data related to the intercepted write I/O operations are stored in a write I/O buffer. One or more levels of inspection are performed on the segments of data stored in the write I/O buffer to detect a security threat. A protection instruction is injected in any segments of data having a detected security threat. The defensive action can be performed for the injected protection instruction prior to storing segments of data in the write I/O buffer in the storage system. The protection instruction can be injected at the head of the segments of data having a detected security threat.

Abstract: Systems and methods for determining similarity between sets of objects are disclosed. A set of hashes are generated for a set of objects. A similarity vector is generated for the set of hashes. The similarity vector is a compact representation of the set of hashes and of the corresponding set of objects. The similarity of the set of objects is determined by comparing the similarity vector of the set of objects with other similarity vectors. In a data protection system, the set of objects can be placed with the node or system that stores objects that are most similar to the set of objects being placed.