Abstract: Disclosed herein are systems and method for determining data storage insurance policies. In an exemplary implementation, a method comprises receiving a request to add a data storage insurance policy to a plurality of data files. The method comprises extracting data file attributes and determining a data recovery score for each respective data file based on a uniqueness, criticality, and/or importance of the respective data file. The method comprises determining a hardware score for each of a plurality of performance tiers comprising at least one storage server, based on an available capacity, a performance cost, and/or data recovery scores of data files currently stored at each of the plurality of performance tiers. The method comprises selecting and executing a data storage insurance policy for the respective data file based on a plurality of data recovery rules and/or the comparison of the data recovery score and the hardware score.

Abstract: A system and method are disclosed for performing non-invasive scan of a target device. The system is configured for: i) loading an endpoint protection agent to a target device; ii) providing a remote direct memory access of the target device to the remote security server for reading a memory of the target device; iii) scanning, by a second memory scan engine of the remote security server, the memory of the target device upon the violation of the security policy; iv) identifying, by the second memory scan engine of the remote security server, a threat on the target device; and v) sending, by the remote security server, a security response action to the endpoint protection agent on the target device in accordance with the security policy.

Abstract: Systems and methods are disclosed for generating a user behavioral avatar. A method may include receiving a request to generate an avatar that performs actions on behalf of a user on a social media platform; identifying a plurality of historical user actions manually taken by the user on the social media platform; generating, based on the plurality of historical user actions, an action profile that represents user tendencies for executing available actions on the social media platform; identifying a plurality of data items in a retrieved backup of at least one computing device associated with the user; classifying the plurality of data items into a plurality of topics; training and executing the avatar to detect, in the social media platform, a social media item that shares at least one topic of the plurality of topics from the backup and perform a user action in accordance with the action profile.

Abstract: An anomaly detection system uses an AI engine to analyze configurations and backups to identify and assess anomalies. Backup data and configurations are used to characterize events as either secure or insecure.

Abstract: Disclosed herein are systems and method for customizing a user workspace environment using artificial intelligence-based analysis. In one exemplary aspect, a method comprises detecting user actions in a user workspace environment that provides access to a plurality of workspace elements. The method comprises generating a plurality of rules for customizing the user workspace environment, wherein each rule links a set of input parameters of a machine learning algorithm as criteria with an output user action and an output identifier of the workspace element, and wherein each rule assigns at least one customization action that (1) reduces an amount of steps to perform in the user workspace environment to access the workspace element associated with the output identifier and (2) reduces a processing time to perform the output user action. When a criterion is fulfilled, the method comprises executing a corresponding customization action that alters the user workspace environment.

Abstract: Disclosed herein are systems and method for customizing a user workspace environment using user action sequence analysis. In one exemplary aspect, a method may comprise detecting user actions in a user workspace environment that provides access to a plurality of workspace elements further comprising a plurality of files and a plurality of applications and identifying a plurality of user action sequences based on each timestamp of a respective user action. The method may comprise generating action sequence groups, each comprising a unique subset of the user action sequences and sequence trigger. In response to detecting a particular sequence trigger, the method may comprise executing a corresponding customization action that alters the user workspace environment such that an amount of steps and/or processing time to perform in the user workspace environment to access workspace elements associated with the associated action sequence group is reduced.

Abstract: A system and method is provided for service level agreement (SLA) based data storage and verification. According to one exemplary aspect, a method includes receiving, from a client device, a request to perform data verification of data relating to a file stored on a remote storage computer; accessing, by a processor, at least one SLA to determine a fault tolerance for the file stored on the remote storage computer; sending, by the processor to the remote storage computer, a request to store k derivatives of the file in the remote storage computer; and transmitting, to the client device, an indication of a location of the k derivatives of the file in the remote storage computer.

Abstract: Disclosed herein are systems and method for determining a backup schedule on a computer system. In one exemplary aspect, a method may comprise collecting user behavior data on the computer system. The method may comprise analyzing the user behavior data to determine an optimal time of a backup session to create backup copies of modified data stored on a volume of the computer system and determining an optimal duration of the backup session based on the analyzed user behavior. The method may comprise determining a portion of the modified data that can be saved during the backup session within the optimal duration at the optimal time of backup, and performing the backup session comprising the portion.

Abstract: Disclosed herein are system and method for driving organization and subsequent analysis of an autonomous vehicle. In an exemplary aspect, the system and method comprise dividing a path of a vehicle into a plurality of segments based on predetermined conditions; monitoring both behavior of the vehicle and driving conditions during each of the plurality of segments; storing the behavior and the driving conditions in a plurality of records of an immutable storage; determining whether an accident has occurred involving the vehicle; in response to determining that the accident has occurred, retrieving for the plurality of segments the behavior and the driving conditions from the immutable storage; reconstructing the path using the retrieved behavior and the driving conditions and the plurality of segments; and analyzing the reconstructed path to determine a cause of the accident.

Abstract: Disclosed herein are systems and method for determining a backup schedule on a computer system. In one exemplary aspect, a method may comprise collecting user behavior data on the computer system and analyzing the user behavior data to determine an optimal time of a backup session to create backup copies of modified data stored on a volume of the computer system. The method may comprise determining an optimal duration of the backup session based on the analyzed user behavior and prioritizing portions of the modified data based on priority rules. The method may comprise determining a prioritized portion of the modified data that can be saved during the backup session based on the duration, computer system hardware and network bandwidth at the optimal time of backup, and performing the backup session comprising the prioritized portion.

Abstract: Disclosed are systems and methods for proactive disaster recovery. The described technique monitors for events raised by a system of interconnected external sensors and other devices for obtaining data on the external environment of servers. The system uses these events as a chain of triggers according to which preventative or preparatory actions for disaster recovery are performed.

Abstract: Aspects of the disclosure describe methods and systems for transmitting data via a satellite to a ground node. In one exemplary aspect, a method comprises splitting, on a satellite, a data segment into a plurality of data chunks, wherein an amount of the data chunks equals a number of ground nodes that the data chunks will be transmitted to. For each respective data chunk, the method comprises determining whether the satellite has a stable connection with the respective ground node. When the satellite has the stable connection with the respective ground node, the method comprises transmitting, by the satellite, the respective data chunk to the respective ground node, and when the satellite does not have the stable connection with the respective ground node, the method comprises transmitting, by the satellite, the respective data chunk to a neighboring satellite for storage until the stable connection is established.

Abstract: A method and system for verified data restoration in storage systems is described. The method may include calculating a hash sum relating to initial data for storing in an archive and storing the data into the archive and storing the calculated initial hash sum into a blockchain network. The method may further include retrieving the data from the archive and calculating a retrieved hash sum of the retrieved data. The method may also include comparing the initial hash sum from the blockchain network with the retrieved hash sum. The method may additionally include restoring the data from the archive if the initial hash sum is equivalent to the retrieved hash sum.

Abstract: Disclosed herein are systems and method for securely providing access to data. In one exemplary aspect, a method may comprise receiving a request to access data on a computing device of a user and identifying a location of the computing device. The method may comprise determining whether access to the data is allowed in the location based on a location-based rule of a plurality of location-based rule. The method may comprise, in response to determining that access to the data is allowed in the location, detecting, via sensors of the computing device, (1) at least one other person different from the user or (2) a surveillance device in the location, and determining whether the at least one other person or the surveillance device can view the data without direct access to the computing device. If not, the method may comprise providing access to the data on the computing device.

Abstract: Disclosed herein are systems and method storing data. In an exemplary aspect, a method comprises determining a threshold data size for objects in a target data system; generating a data blob that has an allocated size of at least the threshold data size, wherein the data blob comprises at least one data set; identifying a data set stored in a source data system for backup to the target data system; determining whether a size of the data set is less than the threshold data size; and responsive to determining that the size of the data set is less than the threshold data size, inserting the data set into the data blob; and writing the data blob to the target data system.

Abstract: Disclosed herein are systems and method for synthetic file scanning. In one exemplary aspect, a method may comprise, for each respective file in a plurality of files on a storage device, determining a respective likelihood of the respective file being targeted by the malicious activity, and including, in a subset of files, each respective file in the plurality of files with a respective likelihood that is greater than a threshold likelihood. The method may comprise for each respective file in the subset of files, identifying at least one fragment of the respective file that is susceptible to the malicious activity. The method may comprise extracting and storing at least one fragment from each respective file in a synthetic file, and scanning the synthetic file periodically instead of the plurality of files.

Abstract: Disclosed herein are systems and method for isolating private information in streamed data. In an exemplary aspect, a method may comprise receiving a stream of data, for storage in a first storage device, and an indication of how the stream will be utilized by an end user. The method may comprise comparing the indication against a plurality of rules, wherein each rule indicates a type of private information that should be isolated from a given input stream based on a respective indication of usage for the given input stream. The method may comprise identifying and extracting a first type of private information that should be isolated from the stream, modifying the stream by removing the first type of private information from the stream, storing the modified stream in the first storage device, and storing the extracted first type of private information in a different location from the modified stream.

Abstract: Disclosed are systems and method for machine learning and blockchain-based anti-discrimination validation. The described techniques uses a machine learning model to generate a numerical determination associated with a first person based on an input data set associated with the first person. The numerical determination is further based on a corrective module configured to compensate for prohibited discrimination by the machine learning model. The technique generates a blockchain transaction data structure comprising a state of the machine learning model at the time of generating the numerical determination, a copy of the input data set associated with the person, and an indication of a correction by the machine learning model. The blockchain transaction data structure is recorded or published in a blockchain network.

Abstract: Disclosed herein are systems and methods for protecting user data. In one aspect, an exemplary method comprises, by a hardware processor, detecting one or more user files, created by a user, that are on a user device; generating user transactional data associated with one or more detected network-based interactions with a service provider by the user, and user behavior data based on one or more user interactions with a graphical user interface of the user device by the user; generating a user classification using a machine learning model that classifies the user based on the one or more user files, the user transactional data, and the user behavior data; and when the user is identifiable based on the user classification, modifying at least one of the one or more user files stored on the user device and user behavior of the user during an operation of the user device.

Abstract: Disclosed herein are systems and methods for protecting user data. In one aspect, an exemplary method comprises, by a hardware processor, detecting one or more user files modified by a user on a user device; identifying user actions executed by the user to modify the one or more user files; training a machine learning algorithm to identify whether an arbitrary user action is performed by the user, wherein the user actions used by the user to modify the one or more user files are comprised in a training dataset of the machine learning algorithm; detecting a user action to modify a user file; determining, using the machine learning algorithm, whether the user action classifies as being performed by the user; and in response to determining that the machine learning algorithm classifies the user action as being performed by the user, modifying the user action to mask an identity of the user.