ML Authentication Models for Enterprise Resource Security
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Solution Overview
Problem
Enterprise organizations face challenges in ensuring the security and integrity of their managed information and resources, particularly when optimizing resource utilization and bandwidth, as unauthorized access can compromise sensitive data.
Innovation Solution
Implementing machine-learning models trained on user interaction data, including unused customer data, to authenticate users and protect enterprise-managed information and resources by validating session-specific interaction data and adjusting authentication parameters dynamically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional authentication methods are used to ensure security, then security is improved, but resource utilization and operational efficiency deteriorate
Solution Approach 1:
The authentication system dynamically adjusts authentication requirements based on risk assessment of user interactions. The machine learning model continuously evaluates interaction patterns and modifies authentication intensity in real-time, allowing efficient processing for low-risk users while maintaining strong security for suspicious activities, thus resolving the contradiction between security and resource utilization
Solution Approach 2:
The system changes authentication parameters dynamically based on analyzed interaction data. When interactions are deemed normal, authentication parameters are relaxed to improve efficiency; when suspicious patterns are detected, parameters are tightened to enhance security. This adaptive parameter adjustment resolves the contradiction by optimizing both security and resource utilization based on actual conditions
2Measurement precision
If machine-learning models are deployed to enhance authentication security, then authentication accuracy is improved, but system complexity increases
Solution Approach 1:
The patent introduces an intermediary authentication service that sits between users and enterprise resources. This service contains the machine learning models and handles all authentication logic, keeping the complexity isolated in one component while presenting a simple interface to users and existing enterprise systems. The intermediary approach allows high authentication accuracy through ML while managing system complexity through clear separation of concerns
Solution Approach 2:
The machine learning models perform self-training and self-adjustment using interaction data from the enterprise environment. The system automatically learns from patterns without requiring manual configuration or intervention, reducing operational complexity while maintaining high authentication accuracy. The self-service nature of the ML models allows them to adapt to new threats and user behaviors autonomously
3Reliability
If unused customer data is utilized for training authentication models, then authentication effectiveness is improved, but data privacy concerns increase
Solution Approach 1:
The system extracts only the necessary interaction patterns and behavioral features from customer data that are needed for authentication, leaving out personally identifiable information and sensitive details. By extracting only the essential authentication-relevant features while discarding sensitive data, the system improves authentication effectiveness using unused customer data while minimizing data privacy concerns through selective data extraction
Data Source
AI summary
Aspects of the disclosure relate to using machine-learning models to authenticate users and protect enterprise-managed information and resources. In some embodiments, a computing platform may receive user interaction data from enterprise computing infrastructure and may train one or more authentication models based on this data. Subsequently, the computing platform may receive, from a first application server, a request to authenticate a first user to a first user account in a first usage session hosted by the first application server. In response to receiving this request, the computing platform may identify whether session-specific interaction data for the first usage session is valid based on the one or more authentication models. If the interaction data is identified as being valid, the computing platform may generate and send one or more commands directing the first application server to allow the first user to access the first user account in the first usage session.