Unified Security Queries Across Subsystems for Unstructured Threat Detection
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Solution Overview
Problem
The complexity of computer attacks is increasing, and existing technologies struggle to effectively process large quantities of unstructured data to identify security threats using Artificial Intelligence and Machine Learning, hindering the ability of good actors to counter malicious activities.
Innovation Solution
A computer-implemented method that establishes connectivity with multiple security-relevant subsystems, receives and distributes unified queries, and generates result sets using AI/ML to analyze structured and unstructured data, combining these results to provide a unified query response.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional security systems are used to process security data, then system simplicity is maintained, but the ability to process large quantities of unstructured data and detect complex threats is insufficient
Solution Approach 1:
The patent combines multiple security subsystems (endpoint detection, network security, cloud security) into a unified threat mitigation platform that integrates various AI/ML processing capabilities. This merging allows the system to handle large quantities of unstructured data from diverse sources while presenting a unified interface to users, thereby improving productivity without proportionally increasing perceived complexity.
Solution Approach 2:
The patent introduces AI/ML processing layers as intermediaries between raw security data and analysis functions. These intermediaries automatically process unstructured data (logs, alerts, threat intelligence) and transform them into structured insights, enabling the system to handle vast data volumes without requiring proportional increases in human operational complexity.
2Measurement precision
If AI/ML processing is implemented to analyze security data, then threat detection effectiveness is improved, but processing time and computational resources increase
Solution Approach 1:
The patent implements pre-processing of security data including normalization, filtering, and feature extraction before main AI/ML analysis. Threat intelligence data is pre-enriched and structured in advance, allowing the core ML models to focus on pattern recognition rather than raw data processing, thereby improving detection accuracy while reducing overall processing time.
Solution Approach 2:
The patent divides the AI/ML processing into multiple specialized modules (anomaly detection, threat classification, behavior analysis) that operate in parallel on different data streams. This segmentation allows simultaneous processing of multiple security events with high accuracy while reducing the time each individual analysis takes by distributing computational load across specialized processors.
3Adaptability or versatility
If multiple security subsystems are integrated into a unified platform, then comprehensive threat coverage is achieved, but system complexity and integration difficulty increase
Solution Approach 1:
The patent creates a universal threat mitigation platform that can interface with multiple different security subsystems through standardized APIs and data formats. The unified query interface accepts generic requests and automatically routes them to appropriate specialized subsystems, allowing comprehensive threat coverage across endpoint, network, and cloud security while maintaining a simple single-point interface for users.
Solution Approach 2:
The patent introduces integration layers and adapter modules that act as intermediaries between diverse security subsystems and the core platform. These intermediaries handle protocol translation, data format normalization, and authentication management, thereby enabling comprehensive multi-subsystem integration while shielding users from the underlying integration complexity.
Data Source
AI summary
A computer-implemented method, computer program product and computing system for: establishing connectivity with a plurality of security-relevant subsystems within a computing platform; receiving a unified query from a third-party concerning the plurality of security-relevant subsystems; distributing at least a portion of the unified query to the plurality of security-relevant subsystems; and effectuating the at least a portion of the unified query on each of the plurality of security-relevant subsystems to generate a plurality of result sets.