Adaptive Authentication Risk Scoring for Secure Data Release
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Solution Overview
Problem
Existing authentication methods for online accounts are insecure due to the ease of forging identification and the availability of personal data, leading to increased instances of hacking and fraud.
Innovation Solution
Implementing adaptive authentication processes that utilize multiple factors, including location verification through GPS, biometric identification, and secure device interactions like ATM transactions, along with machine learning to assess risk scores and create fraud profiles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple authentication factors are used, then security is improved, but device complexity and authentication time increase
Solution Approach 1:
The system performs preliminary risk assessment by collecting contextual information (device characteristics, location data, behavior patterns) before the authentication process begins. This pre-evaluation allows the system to prepare appropriate authentication challenges in advance, reducing on-the-fly complexity during actual authentication.
Solution Approach 2:
The authentication system dynamically adapts its complexity based on real-time risk assessment. Instead of always applying maximum security measures, the system adjusts the number and type of authentication factors required based on the calculated risk score, making the system both secure when needed and simple when safe.
2Reliability
If multiple authentication factors are required, then fraud prevention is improved, but authentication time and user convenience deteriorate
Solution Approach 1:
The system collects and analyzes contextual information (device fingerprints, location history, behavior patterns) before authentication occurs. This preliminary data gathering enables the system to pre-calculate risk levels and prepare appropriate authentication workflows, reducing actual authentication time by avoiding ad-hoc decision-making.
Solution Approach 2:
The authentication time and number of required factors dynamically adjust based on real-time risk assessment. Low-risk transactions may require only one factor (e.g., biometric), while high-risk transactions trigger additional factors, optimizing the balance between security and speed for each specific case.
3Measurement precision
If contextual information is collected for risk assessment, then authentication accuracy is improved, but data privacy concerns and system complexity increase
Solution Approach 1:
The system segments contextual information collection into distinct modules: device characteristics collection, location data collection, behavior pattern analysis, and transaction context gathering. Each module independently processes specific data types and contributes to the overall risk score, making the complex data processing manageable and auditable.
Solution Approach 2:
The system introduces a dedicated risk assessment engine as an intermediary layer between data collection and authentication decision-making. This intermediary processes contextual information, applies risk models, and outputs risk scores without exposing raw user data to authentication systems, thereby managing complexity while maintaining accuracy.
Data Source
AI summary
Systems and techniques for an adaptive authentication system are described herein. In an example, an adaptive authentication system is adapted to receive a request at a first entity from a second entity for secure data of a user, where the second entity is remote from the first entity. The adaptive authentication system may be further adapted to transmit a prompt to a user device associated with the user for authentication of the user and authentication of the request. The adaptive authentication system may be further adapted to receive a response to the prompt and authenticate the user and the request based on the response. The adaptive authentication system may be further adapted to transmit the secure data of the user to the second entity.