Generative AI Threat Detection for Industrial Asset Vulnerabilities

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Solution Overview

Problem

Industrial enterprises face challenges in identifying and managing security vulnerabilities across geographically diverse assets due to the lack of a comprehensive view of their industrial assets and network infrastructure, making them susceptible to cyber intrusions and attacks.

Innovation Solution

An industrial security system leveraging generative artificial intelligence (AI) for automated asset discovery, real-time monitoring, and vulnerability detection, which generates recommendations for mitigating vulnerabilities and deploys countermeasures to vulnerable assets.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of time

If traditional manual methods are used to identify and manage security vulnerabilities across industrial assets, then human operators can analyze and respond to security issues, but the time required to identify and address vulnerabilities increases significantly

Engineering Contradiction:

Improvetime to identify and address vulnerabilitiesVSAvoidvulnerability detection efficiency

Core Design Contradiction:

Loss of timeVSProductivity

Solution Approach 1:

The system enables automated self-service for vulnerability detection and remediation. The generative AI model autonomously analyzes asset data, identifies security vulnerabilities, formulates remedial actions, and implements countermeasures without requiring continuous human intervention. This automation directly reduces the time loss and improves productivity in vulnerability management.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual mechanical analysis methods with an intelligent automated system. The generative AI model substitutes human operators in the vulnerability detection process, using machine learning and natural language processing to analyze asset data, generate security assessments, and recommend remediation strategies, thereby significantly improving detection efficiency.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Loss of information

If comprehensive asset discovery and real-time monitoring are implemented across geographically diverse industrial assets, then security visibility is improved, but system complexity increases

Engineering Contradiction:

Improvesecurity visibility of industrial assetsVSAvoidsystem complexity for asset discovery and monitoring

Core Design Contradiction:

Loss of informationVSDevice complexity

Solution Approach 1:

The system implements a universal multi-functional platform that handles asset discovery, data collection, vulnerability detection, and remediation across diverse industrial assets. The generative AI model serves multiple functions including analyzing asset configuration data, identifying security vulnerabilities, generating remediation recommendations, and communicating with various asset types through a unified interface, thereby improving security visibility without proportionally increasing system complexity.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The patent introduces an intermediary layer consisting of the generative AI model and centralized analysis component that mediates between diverse industrial assets and the security monitoring system. This intermediary standardizes data collection and analysis across different asset types and locations, providing comprehensive security visibility while managing system complexity through abstraction and automation.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Reliability

If automated remedial actions are deployed to mitigate security vulnerabilities in real-time, then security response effectiveness is improved, but the risk of incorrect automated decisions increases

Engineering Contradiction:

Improvesecurity vulnerability mitigation effectivenessVSAvoidrisk of incorrect remediation actions

Core Design Contradiction:

ReliabilityVSObject-affected harmful factors

Solution Approach 1:

The system implements feedback mechanisms where the generative AI model continuously monitors the effectiveness of deployed remediation actions. The model analyzes asset data before and after remediation, assesses whether vulnerabilities have been successfully mitigated, and can adjust or revert actions if negative effects are detected. This feedback loop improves reliability while minimizing the risk of harmful incorrect actions.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent applies preliminary action by having the generative AI model formulate and validate remediation strategies before actual deployment. The system prepares remedial actions, performs virtual assessments, and only implements countermeasures after confirming their appropriateness and safety, thereby reducing the risk of incorrect automated decisions while maintaining effective security response.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS12563084B2Generative AI OPS for cyber security threat detection

Publication Date: 2026.02.24 ROCKWELL AUTOMATION TECH INC

AI summary

An industrial security system leverages generative artificial intelligence (AI) to automate the process of identifying software or hardware insecurities on industrial assets, generate recommendations for mitigating these vulnerabilities, and, where appropriate, deploy countermeasures to the vulnerable assets. By leveraging automated asset discovery, real-time asset and network monitoring, and generative AI-assisted vulnerability detection and remediation, the system can reduce the amount of time spent by security administrators in identifying and closing security vulnerabilities within their plant environments, and can alert administrators of potential security issues before those issues become critical.