AI Resource Ontology for Dynamic Software Authentication
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Solution Overview
Problem
Traditional security systems face challenges in detecting and addressing potential security issues due to the increasing frequency and severity of cybersecurity breaches in automated and digitized organizations.
Innovation Solution
A resource ontology system that uses artificial intelligence and machine learning to monitor and authenticate resources, track updates, and optimize resource usage by creating and managing application signatures and interconnected maps of resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional security systems are used to monitor and authenticate resources, then the system structure is simple and easy to implement, but the system cannot keep up with the increasing frequency and severity of cybersecurity breaches
Solution Approach 1:
An AI/ML-based intermediary security system is introduced between applications and resources. This intermediary continuously monitors resource interactions, analyzes patterns using machine learning, and authenticates resources dynamically. The AI/ML component acts as a mediator that enhances security detection capabilities without requiring complete restructuring of the existing system architecture.
Solution Approach 2:
The system performs preliminary authentication and monitoring of resources before they are accessed by applications. By pre-establishing trust relationships and continuously monitoring resource states in advance, the system can detect and respond to security threats before they compromise the organization's infrastructure.
2Productivity
If manual resource authentication and tracking is performed, then the system is easy to understand and implement, but it is inefficient and cannot dynamically detect resource updates
Solution Approach 1:
The security system enables resources to self-authenticate and self-report their states through automated mechanisms. Resources can independently provide their identity, version, and status information to the AI/ML monitoring system, eliminating the need for manual authentication while maintaining system simplicity through standardized self-service interfaces.
Solution Approach 2:
Manual mechanical authentication processes are replaced with AI/ML-based automated detection and verification systems. The system uses machine learning algorithms to automatically analyze resource signatures, detect updates, and authenticate resources, substituting human-operated mechanical processes with intelligent automated systems that enhance productivity.
3Reliability
If comprehensive monitoring of all resources and applications is implemented, then security coverage is complete, but the time and computational resources required increase significantly
Solution Approach 1:
The AI/ML system implements partial monitoring by focusing computational resources on high-risk interactions and critical resources. Rather than uniformly monitoring all resources at maximum depth, the system dynamically adjusts monitoring intensity based on risk assessment, applying excessive action only where necessary to maintain complete security coverage while reducing overall time consumption.
Solution Approach 2:
The system employs periodic monitoring and analysis cycles instead of continuous full-depth scanning. The AI/ML model periodically analyzes resource interactions and updates its understanding of the environment, allowing the system to maintain comprehensive security coverage while managing computational resources efficiently over time.
Data Source
AI summary
An artificial intelligence (AI) and machine learning (ML) (collectively “AI/ML”) system that provides dynamic detection of potential of resource updates, authentication of the resources updates, and tracking of the links between resources through the use of resource signatures. The resource signatures may provide an indication of the application information, the resources that are accessed by the application, and the resources that access the application. As such, the AI/ML system can monitor and track the applications and updated resources that interact with the applications in order to identify any potential security issues, as well as to optimize and standardize the use of resources by the users when developing applications.