Sensitive Data Compliance Manager for Cross-Database Policy Detection
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Solution Overview
Problem
Existing data compliance tools struggle to provide a unified view of sensitive data across multiple databases, lack data flow path mapping, and fail to generate compliance reports at the database level, making it difficult for organizations to ensure adherence to data protection regulations like GDPR, especially in complex enterprise environments.
Innovation Solution
A compliance manager utilizing advanced machine learning techniques for automated data lineage and neural networks to identify and classify data objects, detect compliance with sensitivity policies, and generate alerts for non-compliant data, offering centralized control and monitoring across distributed databases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional data compliance tools are used, then basic data storage is possible, but unified view of sensitive data across multiple databases cannot be achieved
Solution Approach 1:
The patent introduces a compliance manager as an intermediary system that sits between multiple databases and provides centralized compliance monitoring. This mediator generates data lineage information, classifies data objects using neural networks, and produces compliance reports without requiring direct complex integration between all databases, thus achieving unified visibility while managing system complexity
Solution Approach 2:
The compliance manager segments the compliance monitoring function into distinct components: data lineage generation, data object classification, policy compliance detection, and report generation. This segmentation allows each component to be optimized independently and facilitates the unified view across distributed databases by breaking down the complex task into manageable segments
2Productivity
If manual compliance monitoring is performed, then detailed analysis is possible, but productivity and efficiency decrease
Solution Approach 1:
The system enables self-service compliance monitoring through automated data lineage generation and neural network-based classification. The compliance manager autonomously scans databases, identifies sensitive data objects, classifies them according to policies, and generates compliance reports without requiring manual intervention, thus dramatically improving productivity while maintaining high detection accuracy through sophisticated algorithms
Solution Approach 2:
The patent replaces manual mechanical compliance monitoring with automated computational systems. Neural networks and machine learning algorithms substitute for human analysts in detecting and classifying sensitive data, providing both high-speed processing (improving productivity) and sophisticated pattern recognition (maintaining measurement precision)
3Reliability
If comprehensive data scanning is performed across all databases, then complete compliance coverage is achieved, but processing time and resources increase
Solution Approach 1:
The compliance manager performs preliminary actions by continuously generating and maintaining data lineage information in the background. This preliminary data preparation allows the system to quickly assess compliance status when needed, achieving complete compliance coverage without requiring time-consuming scans at the moment of compliance checking
Solution Approach 2:
The system maintains continuous compliance monitoring through ongoing data lineage generation and periodic neural network classification. This continuous action ensures complete compliance coverage across all databases while distributing the processing load over time, preventing large time losses associated with periodic comprehensive scans
4Ease of operation
If centralized compliance control is implemented, then compliance management is simplified, but system complexity increases
Solution Approach 1:
The compliance manager serves multiple functions within a single centralized system: it generates data lineage, classifies data objects, detects policy violations, and produces compliance reports. This multi-functionality simplifies compliance management by providing all necessary capabilities in one system while managing internal complexity through modular architecture and standardized interfaces
Data Source
AI summary
A method for evaluating data compliance with sensitivity policies is disclosed. The method involves receiving a user input that specifies a data category and searching a network to identify multiple databases. Data objects stored within those databases are identified, and a distinct signature is generated for each data object. A neural network is used to classify the signatures to determine which data objects contain information associated with the specified data category. For those identified data objects, the method detects whether the data complies with applicable sensitivity policies. Alerts are generated for any data objects that match the data category but fail to meet the sensitivity requirements.