Real-Time Machine-Learning Analysis of Digital Profiles for Network Security
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Solution Overview
Problem
Existing network security systems are inherently insecure due to the use of digital identity profiles, leading to potential network breaches and cyberattacks.
Innovation Solution
Implementing machine-learning models to analyze digital metrics in real-time, generating training datasets to train an artificial intelligence agent that identifies potential security threats and generates electronic messages to mitigate risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If digital identity profiles are used for access authentication, then ease of operation is improved, but network security reliability deteriorates
Solution Approach 1:
The patent introduces an intermediary AI agent that sits between the digital identity profiles and the authentication system. This agent continuously monitors profile metrics and only intervenes when anomalies are detected, allowing the profiles to maintain their ease of use while adding a security layer through the intermediary's real-time analysis and alerting mechanisms
Solution Approach 2:
The system implements feedback by continuously monitoring digital identity profiles for changes in behavior patterns and security metrics. The AI agent receives feedback about profile anomalies and triggers alerts when threshold violations occur, creating a closed-loop security system that maintains operational ease while improving security through ongoing monitoring and response
2Reliability
If machine-learning models are applied to analyze digital metrics in real-time, then network security reliability is improved, but device complexity increases
Solution Approach 1:
The patent segments the security monitoring function into a separate AI agent component that operates independently from the main authentication system. This segmentation allows the complex machine-learning analysis to be isolated in a dedicated module, improving security through sophisticated analysis while managing overall system complexity by dividing functions into distinct, manageable components
Solution Approach 2:
The AI agent performs self-service by autonomously monitoring digital identity profiles, detecting anomalies, and generating alerts without requiring constant human intervention. This self-service capability improves network security through continuous autonomous analysis while reducing the operational complexity burden on human administrators
Data Source
AI summary
Disclosed are example methods, systems, and devices that allow for executing machine-learning models for real-time and secure analysis of digital metrics. The techniques include generating metrics for identity elements stored in digital profiles of users. A subset of profiles can be identified that have metrics that fall below a predetermined thresholds, with which a training dataset can be generated. Machine-learning models can be executed over the training dataset to train an artificial intelligence agent that receives digital profiles as input and outputs translational elements corresponding to identity elements in the digital profiles. After training, additional profiles can be input to the machine-learning models of the artificial intelligence agent to identify a second subset of digital profiles with corresponding metrics. Electronic messages corresponding to the second subset can be generated and transmitted to one or more computing devices identified in the second subset of digital profiles.