Behavior-Based Escalated Authentication System
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Solution Overview
Problem
Current authentication systems lack the ability to dynamically adjust security levels based on user behavior, often requiring the same level of authentication for both genuine and suspicious users, which can compromise security and user convenience.
Innovation Solution
An adaptive authentication system that collects telemetry data to analyze user behavior patterns, escalating authentication requirements for suspicious users while maintaining simplicity for genuine users by using machine learning models to compare current interactions with registered data, thereby increasing security on-the-fly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multi-factor authentication is implemented to improve security, then security level is improved, but user convenience deteriorates due to additional authentication steps
Solution Approach 1:
The authentication system dynamically adjusts the required authentication level based on real-time behavior analysis. The system transitions from static authentication (always requiring same factors) to dynamic authentication (adapting factors based on risk assessment), allowing the authentication process to be flexible and context-aware
Solution Approach 2:
The system changes the parameters of authentication by introducing behavior-based risk scoring that modifies which authentication factors are required. Instead of always requiring the same authentication factors, the system adjusts the authentication parameters based on detected anomalies in user behavior patterns
2Reliability
If behavior analysis is added to differentiate users, then security is improved, but system complexity increases due to telemetry collection and analysis
Solution Approach 1:
The behavior analysis system serves multiple functions: it continuously monitors user interactions, establishes baseline patterns, detects anomalies, and feeds risk assessments back to the authentication module. This multi-functional approach consolidates what could be separate complex systems into an integrated solution
Solution Approach 2:
The system automatically collects telemetry data, analyzes behavior patterns, and adjusts authentication requirements without manual intervention. The behavior analysis engine self-regulates by continuously learning from user interactions and autonomously making security decisions based on detected patterns
3Reliability
If authentication requirements are increased for all users, then security is improved, but user experience deteriorates due to loss of simplicity
Solution Approach 1:
The system applies different authentication requirements to different users based on their individual risk profiles. Instead of uniform authentication for all users, the system tailors the authentication experience locally to each user's behavior patterns and risk assessment, providing enhanced security only where needed
Data Source
AI summary
Aspects of the disclosure include an escalated authentication system based on user behavior patterns. A user's behavior pattern on a device is collected and/or learned. The collected or learned pattern can be compared to subsequent behavior patterns to determine whether the current user is genuine or suspicious. Users deemed suspicious are subject to increased authentication requirements, often on-the-fly.