Generative AI Threat Workflow for Adaptive Security Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing threat mitigation systems struggle to effectively address the increasing complexity of computer attacks due to their reliance on predefined rules and signature-based detection, which are limited in identifying new and evolving threats.
Innovation Solution
Implementing a generative AI workflow that includes selecting and arranging multiple generative AI nodes to form a visualized workflow, enabling the definition of iterative, splitting, combining, and conditional paths, and processing input commands to produce workflow results, enhancing the detection and response to security events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If predefined rules and signature-based detection are used, then the system is simple to implement and operate, but it cannot effectively identify new and evolving threats
Solution Approach 1:
The patent implements dynamic threat detection by transitioning from static signature-based rules to adaptive AI models that continuously learn and evolve. The system dynamically adjusts detection parameters, thresholds, and behavioral baselines based on real-time data analysis, enabling it to adapt to new threat patterns without requiring manual rule updates.
Solution Approach 2:
The patent replaces traditional mechanical rule-based detection systems with intelligent AI/ML-based detection mechanisms. Instead of relying on predefined signatures and rigid decision trees, the system uses neural networks and machine learning algorithms to automatically identify patterns and anomalies, substituting mechanical processing with cognitive computing capabilities.
2Reliability
If generative AI nodes are selected and arranged to form a workflow, then the detection capability improves, but the device complexity increases
Solution Approach 1:
The patent segments the complex threat detection task into multiple specialized generative AI nodes, each responsible for specific functions such as data preprocessing, pattern recognition, anomaly detection, and threat classification. This modular architecture allows the system to handle complexity through division of labor while maintaining manageable individual components.
Solution Approach 2:
The patent creates a universal generative AI workflow framework that can handle multiple types of security threats and data formats through a single configurable system. The workflow engine provides multi-functionality by supporting various AI models, data sources, and response actions within a unified architecture, reducing the need for separate specialized systems.
3Adaptability or versatility
If the system monitors activity across multiple computing systems and subsystems, then the coverage of threat detection improves, but the difficulty of detecting and measuring increases
Solution Approach 1:
The patent merges data collection and analysis functions across multiple computing systems and subsystems into a unified threat detection platform. By consolidating logs, events, and security data from diverse sources into a centralized analysis engine, the system achieves comprehensive coverage while simplifying the detection process through unified data processing and correlation.
Data Source
AI summary
A computer-implemented method, computer program product and computing system for selecting two or more generative AI nodes from a plurality of generative AI nodes; and arranging the two or more generative AI nodes to form a generative AI workflow.