Generative AI Trust Score for Device Authentication
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Solution Overview
Problem
User devices are vulnerable to theft or hacking, allowing unauthorized access and potential misuse for nefarious purposes.
Innovation Solution
TrustGPT secures devices by using generative artificial intelligence and sensor data to determine the authorized user, training AI models with synthetic data, and adjusting a trust score based on user behavior and sensor inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional security methods are used, then device security is maintained at basic levels, but the device remains vulnerable to sophisticated theft and hacking attempts
Solution Approach 1:
The patent replaces traditional mechanical security systems (passwords, biometric scanners) with a generative AI system that uses sensor data and autoregressive modeling to dynamically assess user trust. The AI model predicts expected sensor patterns and compares them against actual inputs, creating a behavioral security system that adapts to user habits rather than relying on static authentication mechanisms.
Solution Approach 2:
The security system operates autonomously by continuously monitoring sensor data, predicting user behavior patterns, and automatically adjusting trust scores without requiring explicit user authentication. The system serves itself by using its own predictive models to evaluate whether current sensor patterns match expected behavior, enabling real-time security decisions without external intervention.
2Measurement precision
If comprehensive sensor monitoring is implemented, then user identification accuracy improves, but system complexity and computational requirements increase
Solution Approach 1:
The system performs preliminary actions by pre-training the generative AI model with synthetic sensor data that represents normal user behavior patterns. This pre-training phase creates a knowledge base of expected sensor patterns before actual security monitoring begins, allowing the system to quickly evaluate new sensor inputs against established patterns without requiring complex real-time analysis of every possible scenario.
Solution Approach 2:
The patent uses synthetic data copying to create training datasets that mimic real sensor patterns without requiring actual user data collection. The generative AI model learns from these synthetic copies of sensor behavior patterns, enabling accurate user identification while avoiding the complexity of processing and storing large amounts of real user sensor data.
3Speed
If real-time trust score calculation is performed, then unauthorized access is detected quickly, but computational resources and energy consumption increase
Solution Approach 1:
The system applies partial action by calculating trust scores based on selective sensor data sampling rather than processing every sensor input in real-time. The generative AI model predicts expected sensor patterns and compares only the most relevant deviations from these predictions, reducing computational overhead while maintaining detection speed. The system performs excessive action by continuously monitoring sensor data even when no authentication event is occurring, allowing immediate detection when anomalies arise.
Data Source
AI summary
TrustGPT secures a device by ensuring that only an authorized user is able to use the device. TrustGPT utilizes information received from one or more sensors of the device and generative artificial intelligence to determine that the current user is the authorized user. Without TrustGPT, user devices are susceptible to being stolen or hacked and used for nefarious purposes.