AI Threat Detection Using Attribute Maps for Hidden Indicators
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Solution Overview
Problem
In today's electronic environment, identifying data security threats becomes increasingly difficult due to the use of AI engines by threat actors that obscure indicators of such threats, making them undetectable to the human eye, necessitating a system that can efficiently, accurately, and securely identify these threats and generate vulnerability solutions.
Innovation Solution
A system utilizing a pre-trained AI engine with historical datasets to assign attributes to current data transmissions, generate an attribute map, and dynamically generate vulnerability solutions, including continuous monitoring of platforms like the dark web and internal systems to identify threats like AI-generated deepfakes and malware, and apply its own AI engine to correspond with threat actors to gather data and generate warning notifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If AI engines are used by threat actors to generate threats, then the sophistication and effectiveness of threats improve, but the detectability of threats deteriorates
Solution Approach 1:
The patent introduces an intermediary AI engine that acts as a mediator between the threat actor's AI-generated content and human analysts. This intermediary AI engine analyzes the content, attributes, and contextual factors to detect threats that would be invisible to human eyes, effectively bridging the gap between sophisticated AI-generated threats and human detection capabilities.
Solution Approach 2:
The patent replaces manual human analysis of threat indicators with an automated AI-based detection system. This substitution enables the system to process and analyze complex AI-generated threats at scale, overcoming the limitations of human visual detection and enabling reliable identification of threats that lack traditional visible indicators.
2Productivity
If manual analysis methods are used to identify data security threats, then the system complexity remains low, but the productivity and accuracy of threat identification deteriorate
Solution Approach 1:
The patent implements a self-service system where the AI engine automatically analyzes data transmissions, assigns attributes, generates attribute maps, and identifies threats without requiring manual human intervention for each analysis. This automation dramatically increases productivity while the system manages its own complexity through standardized processes and algorithms.
Solution Approach 2:
The patent transforms the threat detection process by changing from manual parameter analysis to automated multi-attribute analysis. The system evaluates multiple parameters simultaneously (content, attributes, contextual factors) and uses these parameter changes to generate comprehensive attribute maps, enabling high-speed processing without proportionally increasing system complexity.
3Measurement precision
If comprehensive attribute analysis is performed on data transmissions, then the accuracy of threat identification improves, but the computing resources required increase
Solution Approach 1:
The patent performs preliminary actions by pre-defining attribute schemas, data structures, and analysis frameworks before processing data transmissions. This preliminary setup enables the AI engine to efficiently evaluate multiple attributes simultaneously without requiring excessive computing resources during actual threat analysis, as the analytical framework is already in place.
Solution Approach 2:
The patent segments the comprehensive attribute analysis into distinct, manageable components (content analysis, attribute assignment, contextual evaluation, attribute map generation). This segmentation allows the system to process each attribute type efficiently using optimized algorithms, reducing overall computing resource consumption while maintaining high measurement precision through systematic multi-attribute evaluation.
Data Source
AI summary
Systems, computer program products, and methods are described herein for identifying data security threats and dynamically generating vulnerability solutions. The present invention is configured to identify a current data transmission; apply the current data transmission to an artificial intelligence (AI) engine, wherein the AI engine is pre-trained with at least one historical dataset; assign, by the AI engine, at least one attribute to the current data transmission, wherein the at least one attribute comprises a group attribute, technology attribute, an AI attribute, or a network attribute; and generate, based on the assigned at least one attribute to current data transmission, an attribute map, wherein the attribute map comprises an attribute node for each of the at least one attribute, and at least one edge between at least two attribute nodes.