Data Discovery Agents for Privacy Classification Across Data Sources
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Solution Overview
Problem
Existing data privacy management systems struggle to accurately and efficiently classify and manage personal data across various data sources, including structured and unstructured systems, while ensuring compliance with evolving regulations and protecting sensitive information.
Innovation Solution
A data privacy management system that deploys a data discovery agent within a customer network to scan, preprocess, and classify data, using anonymization techniques and machine learning models to identify and categorize sensitive data, while maintaining data security and compliance with privacy laws, and providing proactive risk management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data is scanned and classified across multiple data sources to improve privacy management accuracy, then measurement precision of data classification is improved, but device complexity and time consumption increase
Solution Approach 1:
The patent segments the data classification task by deploying specialized data discovery agents at different network locations (edge devices, gateways, central servers) that each handle specific data sources or classification rules. This distributes the complexity across multiple simple agents rather than requiring one complex centralized system, thereby improving classification accuracy while managing system complexity through modular deployment.
Solution Approach 2:
The patent introduces data discovery agents as intermediary components between data sources and privacy management systems. These agents perform local preprocessing, classification, and anonymization before data leaves the network, acting as mediators that simplify the overall system architecture while maintaining high classification accuracy through distributed intelligence.
2Measurement precision
If comprehensive data scanning is performed across all data sources to improve data discovery completeness, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent applies preliminary action by having data discovery agents continuously monitor and pre-classify data as it enters the network or resides in data sources, rather than performing comprehensive scans only when needed. Metadata about data classifications is maintained and updated in real-time, enabling quick compliance assessments without time-consuming full scans.
Solution Approach 2:
The patent implements partial scanning strategies where data discovery agents focus on scanning only relevant data sources or data types based on specific compliance requirements or risk assessments. This selective approach maintains discovery completeness for critical data while reducing overall scanning time by avoiding unnecessary comprehensive scans of all data.
3Object-affected harmful factors
If data is anonymized and preprocessed before sharing to improve privacy protection, then harmful factors are reduced, but loss of information occurs
Solution Approach 1:
The patent applies local quality by performing anonymization and preprocessing operations selectively based on the specific requirements of each data recipient or use case. Different levels of anonymization are applied to different data elements or datasets depending on their sensitivity and the intended purpose, preserving maximum useful information while providing appropriate privacy protection for each specific context.
Solution Approach 2:
The patent changes parameters of data representation through various anonymization techniques (generalization, aggregation, suppression, perturbation) that transform data into different forms maintaining statistical properties and analytical utility while removing personally identifiable information. This allows data to retain its usefulness for compliance reporting and analysis without exposing individual privacy.
Data Source
AI summary
Disclosed are some techniques for implementing a data discovery agent in a network associated with a customer organization of a data privacy management system. Some implementations relate to one or more connections with one or more data sources storing private data of the customer organization. The one or more data sources can be scanned to obtain scanned data from the private data. The scanned data can be processed, including anonymizing the scanned data, to obtain preprocessed data for use by one or more classification operations. The preprocessed data can be shared with the data privacy management system. One or more classification promotion operations can be performed on classified data elements.