Hybrid Security Tier Engine Using Collaborative Filtering
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Solution Overview
Problem
Existing security systems lack personalized and adaptive methods to determine the appropriate security tier for users based on their unique characteristics, leading to potential security breaches due to either over- or under-secured settings.
Innovation Solution
A hybrid recommendation engine that combines customer demographics and collaborative filtering to suggest a security tier by using enrollment questionnaires to gather user data, applying rule-based and collaborative filtering recommendations, and averaging weighted values to determine a tailored security tier.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a fixed security tier is assigned to all users, then system management is simplified, but security effectiveness deteriorates due to inability to adapt to individual user risk profiles
Solution Approach 1:
The patent implements dynamic security tier assignment by calculating individualized security scores based on user-specific factors including account age, transaction history, device characteristics, and behavioral patterns. The security tier automatically adjusts as user profiles evolve, transforming the static security model into an adaptive one that responds to changing risk profiles while maintaining manageable complexity through automated calculations.
Solution Approach 2:
The system applies differentiated security measures to different user segments based on their specific risk characteristics. Instead of uniform security policies, each user receives a customized security tier tailored to their individual profile, allowing high-risk users to receive enhanced security measures while low-risk users experience streamlined authentication, thereby optimizing security effectiveness across diverse user populations.
2Productivity
If security tier determination is based solely on automated algorithms, then processing speed is improved, but accuracy deteriorates due to lack of human judgment
Solution Approach 1:
The system incorporates feedback mechanisms where security tier assignments and subsequent user actions are continuously monitored. The automated algorithms learn from observed security events, authentication patterns, and threat intelligence to refine their predictions. This feedback loop enables the system to maintain high processing speed while progressively improving accuracy through data-driven adjustments to security scoring models.
3Adaptability or versatility
If multiple security factors are evaluated individually, then assessment comprehensiveness is improved, but system complexity worsens due to multiple evaluation criteria
Solution Approach 1:
The patent consolidates multiple security evaluation factors including account history, device fingerprinting, behavioral biometrics, and transaction patterns into a unified security score calculation framework. By merging these diverse criteria into a single integrated assessment model, the system achieves comprehensive evaluation coverage while maintaining manageable complexity through centralized processing logic that weighs and combines various factors into an overall security tier determination.
Data Source
AI summary
A computer-implemented method, including receiving, by one or more computer systems, customer characteristic information for a user; applying, by the one or more computer systems, one or more recommendation rules to the customer characteristic information to determine a security tier; comparing, by the one or more computer systems, the customer characteristic information to one or more other users with a threshold level of similarity to the user for which the customer characteristic information is received; identifying, by the one or more computer systems, a security tier assigned to one of the one or more other users; and generating information indicative of a recommended security tier, based on the identified security tier and the determined security tier.