Context-Enriched Data Classification for Targeted PII Security
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Solution Overview
Problem
Existing cybersecurity measures for protecting data stores containing personally identifiable information (PII) are burdensome and inefficient, posing a significant risk to businesses and governmental entities due to the resource-intensive nature of securing all data, while cyber threats targeting PII remain a significant concern.
Innovation Solution
Implementing contextually enriched data classifications that identify roles, identifiability, geolocation, and data protection status to classify data portions accurately, allowing for tailored cybersecurity measures and policy enforcement across related datasets, thereby reducing misclassification and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If extreme cybersecurity measures are applied to all data collected, then data protection reliability is improved, but resource consumption and operational burden increase significantly
Solution Approach 1:
The patent applies different security measures to different portions of data based on their classification. Sensitive data such as PII receives enhanced security measures including encryption and access controls, while non-sensitive data receives standard security measures. This localized approach to security quality resolves the contradiction by providing high reliability only where needed rather than uniformly across all data.
Solution Approach 2:
The patent segments data into different classifications (e.g., PII, non-PII, sensitive, non-sensitive) and applies appropriate security measures to each segment. This segmentation allows the system to allocate security resources efficiently, providing strong protection to critical data segments while using minimal resources on less critical segments, thus resolving the contradiction between reliability and resource consumption.
2Reliability
If extreme cybersecurity measures are applied to all data collected, then data protection reliability is improved, but device complexity and operational burden increase
Solution Approach 1:
The patent implements local quality by applying complex security measures only to specific data portions that require them. The system automatically classifies data and applies appropriate security policies, so that only sensitive data receives the full suite of security measures while other data receives simplified protection, reducing overall system complexity.
Solution Approach 2:
The patent employs automated data classification and security policy enforcement systems that self-manage the security operations. The system automatically identifies sensitive data, classifies it appropriately, and applies the correct security measures without requiring manual intervention for each data element, thereby reducing operational burden and managing complexity.
3Measurement precision
If data classification accuracy is improved through contextual enrichment, then security measure effectiveness is improved, but processing time and computational resources increase
Solution Approach 1:
The patent performs data classification and contextual enrichment as preliminary actions during data ingestion or storage operations. By classifying data at the point of entry rather than during subsequent security operations, the system achieves high classification accuracy without adding processing delays to critical security functions later in the data lifecycle.
Solution Approach 2:
The patent merges the data classification function with existing data processing pipelines or storage operations. By combining classification activities with routine data management tasks, the system achieves accurate contextual enrichment without requiring separate processing steps, thereby minimizing additional processing time.
Data Source
AI summary
A system and method for data classification. A method includes: determining a role for each of a plurality of portions of data, wherein the role for each of the plurality of portions of data is defined with respect to a corresponding entity having at least one characteristic represented by the respective portion of data; classifying each of the plurality of portions of data into a plurality of classifications, wherein classifying at least one first portion of data among the plurality of portions of data further comprises applying a classification for at least one second portion of data among the plurality of portions of data to the at least one first portion of data, wherein the role of the at least one first portion of data and the role of the at least one second portion of data match; and performing at least one remediation action based on the plurality of classifications.