AI Device Ownership Attribution for Critical Infrastructure Response
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems lack real-time visibility and scalability to accurately identify and attribute vulnerable devices to their owners across large-scale, complex critical infrastructure systems, hindering effective incident response and remediation efforts.
Innovation Solution
An AI-enabled system utilizing large-language models (LLMs) to discover vulnerable devices, automatically identify owners, and enrich them with sector, location, and point of contact information, providing scalable and actionable business intelligence for rapid incident response.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional device identification methods are used, then system complexity is reduced, but device attribution accuracy and real-time visibility deteriorate
Solution Approach 1:
The patent introduces AI models as intermediary components that bridge raw device data and actionable intelligence. These models process device fingerprints, network traffic patterns, and metadata to generate enriched device profiles and ownership attributions, thereby improving measurement precision without requiring direct complex interactions between all system components
Solution Approach 2:
The system segments the device identification process into distinct functional modules: device discovery, fingerprinting, AI-based attribution, and enrichment. Each module handles specific tasks independently, allowing the system to achieve high attribution accuracy through specialized processing while maintaining manageable overall complexity through modular architecture
2Productivity
If manual device owner identification is used, then system complexity is reduced, but productivity and incident response time deteriorate
Solution Approach 1:
The system implements self-service capabilities where AI models automatically perform device attribution and owner identification without human intervention. The models continuously learn from data and autonomously generate device profiles, enrich information, and support incident response, thereby dramatically improving productivity while the automation complexity is encapsulated within the AI system boundaries
Solution Approach 2:
The system performs preliminary actions by pre-processing device data, creating fingerprints, and generating baseline profiles before incidents occur. This advance preparation enables rapid incident response by having attribution mechanisms already in place and trained, eliminating the need for manual identification during time-critical security events
3Measurement precision
If comprehensive device data collection is implemented, then measurement precision is improved, but loss of time for data processing increases
Solution Approach 1:
The system applies partial action by collecting and processing only the most relevant device data attributes needed for accurate attribution, rather than attempting to process all possible device information. The AI models are trained to derive high-precision identifications from selective key features, reducing data processing time while maintaining identification accuracy
Solution Approach 2:
The patent replaces traditional mechanical data processing methods with AI-based intelligent processing. Neural networks and machine learning models efficiently analyze device data patterns, recognizing relationships and making attributions faster than conventional rule-based or manual processing could achieve, thereby reducing data processing time while improving precision
Data Source
AI summary
Various techniques for providing artificial intelligence-enabled (AI-enabled) device ownership identification for securing nationwide critical infrastructure systems are disclosed. In some embodiments, a system/process/computer program product for providing artificial intelligence-enabled (AI-enabled) device ownership identification for securing nationwide critical infrastructure systems includes discovering vulnerable devices across a plurality of networks; automatically identifying device owners using a large-language model (LLM); and automatically enriching the discovered vulnerable devices with sector, location, and point of contact (POC) information.