AI-Based Field Device Access Decisions for Secure User Management
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Solution Overview
Problem
The administrative effort required to manage user access for field devices in industrial plants is significant, and existing technologies lack efficient methods for automating the decision-making process for adding new field devices to a user management system, which can lead to errors and security risks.
Innovation Solution
A computer-implemented process for training an AI model using identification and function data of field devices, where the AI model learns to determine whether a field device is allowed to log into a user management system through supervised learning, enabling reliable and efficient decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual user management for field devices is implemented, then security and authorization control are improved, but administrative effort and time consumption increase significantly
Solution Approach 1:
The system enables field devices to automatically register themselves with the user management system by sending self-registration requests containing their device information. This self-service mechanism eliminates the need for manual administrator intervention in the registration process, significantly reducing administrative effort while maintaining security through automated authentication and authorization checks.
Solution Approach 2:
The patent replaces manual administrative processes with an automated AI-based system. The AI model automatically evaluates self-registration requests, determines appropriate user roles and authorizations, and manages device access without human intervention. This substitution of manual mechanical processes with automated intelligent systems resolves the contradiction between security control and administrative time consumption.
2Loss of time
If automated decision-making for field device registration is implemented, then administrative effort is reduced, but reliability and security may deteriorate due to lack of human judgment
Solution Approach 1:
The AI model implements a feedback mechanism where it continuously learns from registration decisions and their outcomes. The system evaluates self-registration requests, makes automated decisions, and refines its decision-making based on feedback from successful registrations and security incidents. This feedback loop ensures that automated decisions maintain or improve reliability over time while reducing administrative effort.
Solution Approach 2:
The AI-based automated system performs decision-making independently without requiring human judgment for each registration request. The system autonomously evaluates device information, determines authorization levels, and manages access rights, thereby reducing administrative effort while maintaining reliability through consistent application of security policies and continuous learning.
3Ease of manufacture
If traditional user management systems are used, then implementation simplicity is maintained, but adaptability to new field devices and scenarios is limited
Solution Approach 1:
The AI-based user management system is designed to be universal and adaptable to various types of field devices and automation scenarios. It can process self-registration requests from different device types, determine appropriate user roles based on device characteristics, and manage diverse authorization requirements. This multi-functional capability maintains implementation simplicity while significantly improving adaptability to new devices and scenarios.
Solution Approach 2:
The system adapts to new field devices by dynamically adjusting parameters such as user roles, authorization levels, and access rights based on device information provided in self-registration requests. The AI model learns from varying device parameters and configurations, enabling it to handle diverse device types and scenarios while maintaining a consistent and simple implementation approach.
4Reliability
If manual verification of each field device registration is performed, then security is improved, but productivity and efficiency deteriorate
Solution Approach 1:
The patent replaces manual verification processes with an AI-based automated verification system. The AI model automatically evaluates self-registration requests, verifies device identities, determines appropriate authorizations, and manages access rights without human intervention. This substitution maintains security through intelligent verification while dramatically improving registration efficiency and productivity.
Solution Approach 2:
Field devices perform self-verification by providing their own identification information in self-registration requests. The AI system automatically verifies this information and makes authorization decisions without requiring manual security checks. This self-service verification mechanism maintains security standards while eliminating the productivity loss associated with manual verification processes.
Data Source
AI summary
The present disclosure comprises a computer-implemented process for training an AI model, comprising providing training data, wherein the training data comprise input data and output data. The input data comprise identification and/or function data of a plurality of field devices, and the output data each comprise a result associated with the field devices as to whether or not logging into a user management system was permitted. The training data is fed to the AI model. The process also includes training the AI model using machine learning based upon the training data to identify one or more relationships between the identification and/or function data and the associated results, and using the trained AI model in a process for user management of field devices in automation technology.