AI Threat Mitigation Data Routing for Complex Threat Analysis
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Solution Overview
Problem
The complexity of computer attacks is increasing, making it challenging for good actors to effectively process and mitigate threats using traditional methods, and there is a need for advanced technologies that can understand and address these complexities.
Innovation Solution
A computer-implemented method utilizing Artificial Intelligence (AI) and Machine Learning (ML) to process platform information from security-relevant subsystems, parse and enrich data, identify less threat-pertinent content, and route it to a long-term storage system, while allowing third-party access for further analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional methods are used to process security data, then the system is simpler to operate, but the ability to effectively process and mitigate complex threats deteriorates
Solution Approach 1:
The patent introduces AI/ML algorithms as intermediary components between raw security data and threat mitigation decisions. These algorithms process and interpret complex security data, transforming it into actionable intelligence that traditional systems cannot handle effectively, thereby resolving the contradiction between handling complex threats and maintaining system simplicity.
Solution Approach 2:
The patent replaces traditional mechanical rule-based security systems with AI/ML-based intelligent systems. This substitution enables the system to automatically learn and adapt to complex threat patterns without requiring manual configuration of complex rules, thus improving adaptability while managing complexity through automation.
2Measurement precision
If all platform information is retained for analysis, then measurement precision is improved, but loss of time and computational resources increases
Solution Approach 1:
The patent extracts and identifies less threat-pertinent content from the processed platform information using AI/ML algorithms. By separating and removing non-critical data elements, the system maintains high detection accuracy for relevant threats while reducing the overall data volume that requires intensive processing and storage, thus resolving the time loss contradiction.
Solution Approach 2:
The patent applies different processing quality levels to different portions of data based on their threat relevance. Critical security data receives high-precision processing, while less pertinent data receives simplified processing or is routed to long-term storage, optimizing the balance between detection accuracy and processing efficiency.
3Productivity
If AI/ML is used to process large quantities of security data, then productivity is improved, but device complexity increases
Solution Approach 1:
The patent segments the security data processing system into distinct functional modules: data collection from multiple security subsystems, AI/ML-based processing, threat identification, and response execution. This segmentation allows the complex AI/ML functionality to be integrated in a manageable way, improving productivity while controlling architectural complexity through modular design.
Data Source
AI summary
A computer-implemented method, computer program product and computing system for: receiving platform information from a plurality of security-relevant subsystems; processing the platform information to generate processed platform information; identifying less threat-pertinent content included within the processed content; and routing the less threat-pertinent content to a long term storage system.