Abstract: In one aspect, a computerized system for securing data cloning and sharing options on data warehouses, comprising: a clone determiner engine that determines that a data asset is a primary data asset or a clone data asset, wherein the clone determiner engine comprises: a log data analyzer that obtains and analyzes a set of logs of the data asset from a specified log source, and wherein set of logs are used to determine that the data asset is the primary data asset or the clone data asset, a timestamp analyzer engine that obtains a timestamp data of the data asset and reviews the timestamp data to analyze ordering of the data asset, and wherein the timestamp analyzer engine determines the data asset is a primary asset or a secondary asset, and a fingerprints analyzer that obtains and reviews the data asset and any metadata of data asset, and wherein the fingerprints analyzer creates a fingerprint based on a content of the data asset and the metadata of the data asset, and wherein the fingerprints analyzer then

Abstract: In one aspect, a computerized method for detecting hiding and data intelligence gathering in a data lake or a cloud warehouse, comprising: implementing a hiding and data intelligence collection analysis phase in the data lake or the cloud warehouse; implementing a discovery process in the data lake or the cloud warehouse; implementing a data gathering process in the data lake or the cloud warehouse; and performing one or more dynamic masking operations to detect a Dynamic Masking column anomalies and to detect one or more atypical commands in the data lake or the cloud warehouse.

Abstract: In one aspect, a computerized method for attack generation on a data lake, comprises: for a data lake repository: providing an attack generation mimicry tool; with the attack generation mimicry tool: implementing a reconnaissance phase attack generation; implementing an infiltration phase attack generation on the data lake repository; implementing a hiding and data intelligence collection phase of the attack by hiding from any monitoring or notification system of the data lake repository and surveying the data lake repository to determine what data is worth abusing or exfiltrating from the data lake repository; implementing data gathering phase of the attack that gathers data about other objects, attributes, and relationships in the data lake repository; and implementing the exfiltration of the data or the abuse of the data.

Abstract: In one aspect, In one aspect, a computerized method for automatic grading, impact analysis and mapping to the CIA triad, comprising: identifying a value of a plurality of data stores; associating the value back to an attack scenario such that a measure of impact with respect to attack progression or susceptibility now has a pecuniary value and generating a grading score; associating the grading score mapped to Confidentiality, Integrity and Availability (CIA) Triad; associating an attack progression with the pecuniary value and priority; identifying a progression of the attack; determining a time that is available for a response before a damage occurs to a system under attack; determining a stage of the attack in an attack kill chain, wherein for every stage of the attack as the progress happens, associating the pecuniary value and an impact for such a stage; and automatically calculates an overall data threat grade of the system.

Abstract: In one aspect, a computerized method for detecting data abuse and data exfiltration in a data store or a data lakes cloud warehouse, comprising: identifying a plurality of Command and control (CnC) channels in an enterprise data cloud infrastructure; identifying and detecting malicious compressed data transfers and encrypted data transfers; implementing a destination analysis from within the data store; and implementing data abuse detection and prevention operations.