Iterative Sampling for Data Sensitivity Classification
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Solution Overview
Problem
Organizations face challenges in efficiently categorizing and protecting sensitive data due to the large volumes of data they manage, leading to difficulties in tracking sensitivity characteristics, which can result in costly and time-consuming scanning processes that may lose sensitivity information.
Innovation Solution
The implementation of iterative sampling algorithms that select items to scan based on previously extracted sensitive information, providing reliable statistics on data sensitivity, and updating these statistics until a specified threshold is reached, allowing for efficient data security classification and compliance with regulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive scanning of all data items is performed to ensure complete sensitivity detection, then measurement precision is improved, but productivity deteriorates due to time-consuming processes
Solution Approach 1:
The patent applies partial action by performing iterative sampling on a subset of data items rather than scanning all data comprehensively. The sampling process continues for a limited number of iterations or until convergence criteria are met, providing sufficient sensitivity detection without the overhead of complete scanning of all data items.
Solution Approach 2:
The patent uses preliminary action by performing initial sampling iterations to identify sensitive data patterns and characteristics before conducting further scanning. The system learns from early samples to inform subsequent scanning decisions, improving efficiency while maintaining detection accuracy.
2Productivity
If iterative sampling is performed to reduce computational resources, then productivity is improved, but loss of information worsens due to potential sensitivity information loss
Solution Approach 1:
The patent implements feedback by continuously monitoring sampling results and adjusting the sampling strategy based on detected sensitivity patterns. The system uses feedback from each iteration to refine subsequent sampling decisions, ensuring that sensitive information is captured while minimizing unnecessary scanning. The process continues until convergence criteria indicate sufficient confidence in the sensitivity classification.
Data Source
AI 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.