Abstract: A method for managing composed information handling systems includes receiving, by a system control processor, a mirroring policy from a system control processor manager, receiving an application write request from a first application instance, based on the application write request and the mirroring policy: initiating servicing of the application write request by a first composed information handling system, initiating servicing of an SCP write request by a second composed information handling system, wherein the SCP write request is based on the application write request, receiving a SCP response from the SCP write request from a second system control processor, wherein a second composed information handling system comprises the second system control processor and is executing a second application instance of the cluster application, and sending a write response to the first application instance, wherein the write response is based, at least in part, on the SCP response.

Abstract: One example method includes a splitter for data protection operations including replication operations. The splitter is located on or integrated into a smart network interface card and operates in two modes. The splitter is controlled by a replication controller. In one mode, the control path and the data path both go to the replication controller. In another mode, the data path is directly from the splitter to one or more targets without passing through the replication controller.

Abstract: One example method includes implementing a function as a service (FaaS) at a datacenter by performing operations including receiving an application program interface (API) gateway call from a client application, wherein the API gateway call is associated with an object PUT request, and automatically triggering, with the API gateway call, performance of an object insertion function. The object insertion function includes retrieving, from backend object storage, a previous version of the object, differentially compressing the object relative to the previous version of the object so as to generate a differential, and storing the differential in the backend object storage.

Abstract: Data protection operations including recovery operations are disclosed. A recovery operation is performed by downloading the backup to be recovered from multiple identical copies of the backup. Based on factors such as throughput, an optimal amount of data can be downloaded from each of the multiple copies. The portions downloaded from the multiple copies are rebuild or reassembled once downloaded. The assembled backup is then presented to a target for recovery.

Abstract: One example method includes determining a respective available data storage capacity for each of the edge sites, receiving available data storage capacity information from each of the edge sites, storing the available data storage capacity information, creating a backup dataset, determining whether the group of edge sites have an aggregate amount of available storage capacity to store the backup dataset, and storing the backup dataset across the edge sites when the aggregate amount of available storage capacity is sufficient to store the entire backup dataset.

Abstract: One example method includes receiving multiple raw data streams, each of the raw data streams including data generated and/or collected by a respective IoT device, storing the data of the raw data streams, splitting the data of the raw data streams into ‘N’ storage data streams, and transmitting each of the storage data streams to a different respective storage node. In this example, one of the storage nodes is a production edge node that retains one of the storage data streams, and each of N?1 other storage nodes receives a respective one of the storage data streams.

Abstract: Described is a system for intelligently distributing the retrieval of recovery data amongst peer-based and cloud-based storage sources. The system may leverage a peer network by performing a recovery from a combination of sources including peer devices and a cloud-based storage by considering a multitude of factors. For example, the system may consider retrieval information (e.g. availability, performance metrics, retrieval costs, etc.) associated with each of the potential sources in combination with target information (e.g. recovery objectives, file recovery priorities, etc.) to create an ordering preference (e.g. ranking) for sources from which to retrieve recovery data. The system may rank the sources by determining a specialized recovery score for each source thereby allowing the intelligent retrieval of recovery data.

Abstract: A request is received from a client seeking to access files stored at a backup server. A first tree is received for the request. The first tree represents hashes of files stored at the client. A second tree is generated representing hashes of the files stored at the backup server. The first and second trees are compared to assess a degree of similarity between the files stored at the client and the files stored at the backup server. The user is denied access to the files stored at the backup server when the degree of similarity is below a threshold.

Abstract: First and second trees having leaves identified by hexadecimal values are generated. First files from a first file set are allocated across the first tree based on hashes of the first files. The hashes of the first files are translated into first leaf index values. Second files from a second file set are allocated across the second tree based on hashes of the second files. The hashes of the second files are translated into second leaf index values. The first and second leaf index values are compared to identify leaves that are the same between the first and second trees. A similarity index indicating a degree of similarity between the first and second sets of files is created based on the comparison.

Abstract: Described is a system for intelligently storing and recovering backup data using multiple storage tiers of a cloud-based storage. The intelligent storing of backup data may leverage different cost structures associated with various cloud-based storage tiers by efficiently distributing data to more cost effective tiers. The system may also provide a coordinated (e.g. phased) recovery that prioritizes the recovery of certain files so that a user can gain access to such files in a shorter amount of time. The system may also provide enhanced storage and recovery functionality (e.g. via specialized user interface elements) by leveraging placeholder files to offload and recover individual files, and expedite the recovery of individual files during a substantial recovery process.

Abstract: Described is a system that efficiently detects ransomware attacks within a storage environment. The system may perform a specialized validation by comparing a sampling of backup data obtained from a storage environment with a sampling of data maintained by a specialized validation database. Accordingly, if there is a discrepancy between the samples, the system may issue an alert indicating the original backup data may be encrypted as part of a ransomware attack. The system may utilize the specialized sampling as a validation technique in addition, or as an alternative, to relying on data fingerprints for validation. For example, malicious code may be configured to cause the storage environment to provide fingerprints prior to an unauthorized encryption as an attempt to deceive certain validation processes. Accordingly, to counteract such attempts, the system may rely on the sampling of data, instead of relying solely on a fingerprint comparison.

Abstract: In a public cloud that stores data in a database system for a plurality of entities as primary data and as one or more secondary backup copies of the primary data, the data being stored in predefined data fields of data records, personal private data of each entity is stored encrypted using an encryption/decryption key that is unique to each different entity. The encryption/decryption keys are stored in the cloud in a key store of a key management system. To delete the personal private data of a particular entity, as to comply with the right to be forgotten pursuant to GDPR regulations, or otherwise, the encryption/decryption key for that particular entity is deleted from the key store to render permanently inaccessible all copies of that entity's personal private data.

Abstract: Disclosed is a method, apparatus, and system for collecting video data relating to a security event at a security operations center (SOC), the operations comprising: receiving video data captured through one or more cameras installed at one or more vehicles within a predetermined range of the security event at the SOC from the one or more vehicles; and compiling the video data into viewable format usable in an investigation of the security event.

Abstract: Described is a system for intelligently distributing the retrieval of recovery data amongst peer-based and cloud-based storage sources. The system may leverage a peer network by performing a recovery from a combination of sources including peer devices and a cloud-based storage by considering a multitude of factors. For example, the system may consider retrieval information (e.g. availability, performance metrics, retrieval costs, etc.) associated with each of the potential sources in combination with target information (e.g. recovery objectives, file recovery priorities, etc.) to create an ordering preference (e.g. ranking) for sources from which to retrieve recovery data. The system may rank the sources by determining a specialized recovery score for each source thereby allowing the intelligent retrieval of recovery data.

Abstract: Described is a system that efficiently detects ransomware attacks within a storage environment. The system may perform a specialized validation by comparing a sampling of backup data obtained from a storage environment with a sampling of data maintained by a specialized validation database. Accordingly, if there is a discrepancy between the samples, the system may issue an alert indicating the original backup data may be encrypted as part of a ransomware attack. The system may utilize the specialized sampling as a validation technique in addition, or as an alternative, to relying on data fingerprints for validation. For example, malicious code may be configured to cause the storage environment to provide fingerprints prior to an unauthorized encryption as an attempt to deceive certain validation processes. Accordingly, to counteract such attempts, the system may rely on the sampling of data, instead of relying solely on a fingerprint comparison.

Abstract: Described is a system for intelligent and reversible data masking of computing environment information shared with an external system. The system may leverage a secure masking agent that acts as an intermediary between a computing system (or environment) and an analytics component. The masking agent may provide real-time reversible data masking that ensures that sensitive information is not exposed outside of a secure (e.g. on-premises) environment, while at the same time ensuring the analytics component receives sufficient contextual information to perform a detailed analysis with the shared information. For example, the system may identify and mask identifying information of a particular server or host, while still retaining certain contextual information such as a network topology.

Abstract: In a public cloud that stores data in a database system for a plurality of entities as primary data and as one or more secondary backup copies of the primary data, the data being stored in predefined data fields of data records, personal private data of each entity is stored encrypted using an encryption/decryption key that is unique to each different entity. The encryption/decryption keys are stored in the cloud in a key store of a key management system. To delete the personal private data of a particular entity, as to comply with the right to be forgotten pursuant to GDPR regulations, or otherwise, the encryption/decryption key for that particular entity is deleted from the key store to render permanently inaccessible all copies of that entity's personal private data.

Abstract: A method and system for a cloud backup service leveraging peer-to-peer data recovery. Specifically, the disclosed method and system entail the implementation of a backup-as-a-service (BaaS) that, at least in part, extends the recovery of data through peer-to-peer communications. In an enterprise organization, users often share data files and, accordingly, maintain local copies of these data files on their respective computing devices. Recovery of data, through peer-to-peer communications, may involve the retrieval of these maintained local copies.

Abstract: One example method includes receiving user input concerning a data protection policy for an entity, where the user input is in the form of written input and/or audio input, when the user input comprises audio input, translating the audio input into text, determining an intention of the user with respect to data protection for the entity by translating the text to common terms by interpreting terms of the text that have the same meaning as being similar words and applying context to the words, identifying a data protection policy that best corresponds to the intention of the user concerning data protection for the entity, and applying the data protection policy to the entity.

Abstract: A method, apparatus, and system for storing data at a multi cloud-based storage system is disclosed. The operations comprise: receiving a data block at a first cloud of a multi cloud-based storage system for storage, the multi cloud-based storage system comprising a first number (n) of clouds; generating the first number (n) of coded blocks at the first cloud based on the data block, wherein the data block is recoverable from any second number (k) out of the first number (n) of coded blocks, and wherein the second number (k) is greater than 1 and less than the first number (n); and distributing, by the first cloud, the first number (n) of coded blocks to the first number (n) of clouds of the multi cloud-based storage system, each of the clouds including the first cloud receiving a respective one of the first number (n) of coded blocks for storage.