Continuous Anonymous Risk Evaluation with Threshold-Based Identity Reveal
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Solution Overview
Problem
Current methods for identifying insider threats in organizations, such as financial stress leading to data breaches, are inefficient and risk revealing the identity of individuals, especially in contexts requiring security clearances, due to frequent credit monitoring that violates privacy regulations.
Innovation Solution
A monitoring system uses multiple identifiers (first, second, and third) to anonymize data, generating a third identifier for risk reports only when thresholds are exceeded, storing mappings securely, and revealing identities only when necessary to comply with privacy regulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If frequent credit monitoring is conducted to identify insider threats, then risk detection capability is improved, but individual privacy is compromised and identity exposure increases
Solution Approach 1:
The patent introduces a de-identified credit report as an intermediary that carries risk information without exposing personal identifiers. The credit report is stripped of direct PII and replaced with de-identified markers, allowing the monitoring system to analyze risk indicators while preventing direct identity exposure. This mediator layer enables risk detection without the harmful side effect of revealing individual identities during the monitoring process.
Solution Approach 2:
The patent segments the credit monitoring process into distinct phases: initial de-identification phase where PII is removed and replaced with markers, monitoring phase where only de-identified data is analyzed, and conditional revelation phase where identities are revealed only when specific risk thresholds are exceeded. This segmentation allows the system to maintain continuous monitoring capability while limiting identity exposure to only when absolutely necessary.
2Reliability
If traditional periodic credit report retrieval is used, then insider threat monitoring is performed, but the monitoring efficiency is reduced and credit files are unnecessarily hit
Solution Approach 1:
The patent performs preliminary de-identification of credit reports before they are used for monitoring purposes. By removing PII and replacing it with de-identified markers in advance, the system prepares the data for efficient automated analysis without requiring repeated manual retrieval of full credit reports. This preliminary processing enables continuous monitoring at higher efficiency while reducing unnecessary credit file hits.
Solution Approach 2:
The patent creates de-identified copies of credit reports that contain all necessary risk information but without personal identifiers. These copies can be analyzed repeatedly without impacting the individual's credit file or requiring access to the original identified reports. The de-identified copies serve as efficient surrogates for continuous monitoring while preserving the integrity and privacy of the original credit data.
3Loss of information
If PII is exchanged between databases for data integration, then data linkage capability is improved, but privacy security is compromised
Solution Approach 1:
The patent uses de-identified markers as intermediaries to link data between databases without exchanging actual PII. The de-identified credit report contains markers that can be used to associate risk information with specific individuals internally, while the external data exchange only involves these anonymized markers. This intermediary mechanism maintains data linkage capability necessary for monitoring while preventing the harmful exchange of sensitive personal information between systems.
Solution Approach 2:
The patent creates and exchanges copied, de-identified versions of credit data rather than the original PII-containing records. These copies retain all structural and analytical properties needed for data linkage and risk assessment, but with personal identifiers removed or replaced. The exchange of these copied de-identified records enables database integration and cross-referencing while maintaining privacy security by never transmitting actual PII between systems.
Data Source
AI summary
Techniques for risk evaluation include receiving, from a requesting entity, a request for monitoring target entities specifying a first identifier associated with each target entity and target entity information. The system generates a second identifier and a third identifier for each target entity and stores a mapping of the second identifiers to the first identifiers and the third identifiers, preventing the second identifiers from being provided to the requesting entity. The system monitors a periodically updated data set and determines risk metrics for the target entities, comparing each risk metric to a threshold value to identify target entities whose risk data indicates an insider threat. The system generates a third identifier for the identified target entities and provides the third identifiers to the requesting entity. Responsive to a request for a corresponding first identifier, the system identifies and provides the first and third identifiers to the requesting entity.