Data security classification sampling and labeling

Cybersecurity and data categorization efficiency are enhanced by providing reliable statistics about the number and location of sensitive data of different categories in a specified environment. These data sensitivity statistics are computed while iteratively sampling a collection of blobs, files, o...

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Bibliographic Details
Main Authors Salman, Tamer, Kraus, Naama, Bashir, Salam
Format Patent
LanguageEnglish
Published 18.07.2023
Subjects
CALCULATING
COMPUTING
COUNTING
ELECTRIC DIGITAL DATA PROCESSING
PHYSICS
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Summary:Cybersecurity and data categorization efficiency are enhanced by providing reliable statistics about the number and location of sensitive data of different categories in a specified environment. These data sensitivity statistics are computed while iteratively sampling a collection of blobs, files, or other stored items that hold data. The items may be divided into groups, e.g., containers or directories. Efficient sampling algorithms are described. Data sensitivity statistic gathering or updating based on the sampling activity ends when a specified threshold has been reached, e.g., a certain number of items have been sampled, a certain amount of data has been sampled, sampling has used a certain amount of computational resources, or the sensitivity statistics have stabilized to a certain extent. The resulting statistics about data sensitivity can be utilized for regulatory compliance, policy formulation or enforcement, data protection, forensic investigation, risk management, evidence production, or another classification-dependent or classification-enhanced activity.
Bibliography:Application Number: US201916424539