Sensitive Data Compliance Manager PII Discovery
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Solution Overview
Problem
Current systems face challenges in accurately discovering and associating personal identifying information (PII) across multiple locations within an organization, leading to incomplete and incorrect subject profiles due to limitations in data discovery and False Positive mitigation techniques, which can result in non-compliance with data privacy regulations and steep fines.
Innovation Solution
The system employs a method to search and analyze PII by using known identifying data elements to locate additional related data, utilizing a bipartite graph to visualize relationships between data subjects, locations, and entities, and an interactive dashboard for human analysts to explore and validate associations, incorporating advanced pattern matching and checksum techniques to ensure accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If advanced pattern matching and checksum techniques are used to verify PII accuracy, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent introduces checksum values as intermediary verification elements that simplify the complexity of accurate PII identification. Instead of implementing complex verification logic throughout the system, checksums provide a standardized intermediary mechanism for validating data integrity and identifying PII across different locations and formats.
Solution Approach 2:
The system changes the parameter of data representation by transforming PII into standardized formats with associated checksum values. This parameter transformation enables accurate identification and verification without requiring complex matching logic, as the checksum serves as a reliable identifier that can be efficiently compared and validated.
2Loss of information
If comprehensive data discovery is performed across multiple locations, then quantity of information increases, but loss of time increases
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing checksum values for PII data across the organization before discovery is needed. When a subject profile needs to be completed, the system can quickly search using these pre-prepared checksums rather than performing comprehensive analysis of all data in real-time, significantly reducing discovery time while maintaining completeness.
Solution Approach 2:
The system creates copies of PII data in a standardized format with checksums stored in a central location. These copies enable rapid searching and verification without needing to access and analyze the original data sources directly, reducing the time required for comprehensive data discovery while maintaining information completeness.
3Reliability
If manual validation by human analysts is implemented, then reliability improves, but productivity decreases
Solution Approach 1:
The patent implements feedback mechanisms where the system automatically presents potential PII associations to human analysts with confidence scores and verification options. Analysts can validate or correct associations, and this feedback is used to improve the system's accuracy over time. The checksum-based verification provides automated feedback that reduces false positives, allowing analysts to focus on edge cases and improving both reliability and productivity.
Data Source
AI summary
Techniques for finding and associating personal identifying information with an individual. In one embodiment, a method includes searching a database of personal identifying information held by an organization for instances of a particular item of personal identifying information of a data subject. The database may link personal identifying information to locations at which that personal identifying information is held by the organization. After a storage location with a found instance of the particular item of personal identifying information of the data subject is determined, additional personal identifying information of potential relevance to the data subject may be found at the storage location and used for further searching of the database for more personal identifying information of potential relevance to the data subject at other locations. Personal identifying information may be associated with the data subject and included in a data subject profile.