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, 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 reconnaissance and infiltration in data lakes and cloud warehouses, comprising: monitoring a SaaS data store or a cloud-native data store from inside the data store; examining the attack and automatically identifies how far the attack has progressed in the attack lifecycle; identifying the target and scope of the attack evaluates how far the attackers have penetrated the system and what is their target; and establishing the value of the asset subject to the attackers' attack and maps the impact of the attack on the CIA (confidentiality, integrity and availability) triad.

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.

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 minimizing a data governance in order to improve data security, comprising: providing and imposing a set of access rules to a set of data, wherein the set of data is stored in a data warehouse; measuring a level of over provisioning of the set of data; measuring a level of data abuse susceptibility of the set of data; implementing a dark data governance operation on the set of data; and identifying a set of infrequently used roles in the set of data.