Reasoning-Based Workflow Management for Security Incident Prioritization
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Solution Overview
Problem
Businesses face challenges in efficiently managing security threats due to the complexity of multi-step processes and deciding which threat to investigate first, leading to costly delays.
Innovation Solution
A computer-implemented method for reasoning-based workflow management that detects security incidents, generates threat scores, predicts future system conditions based on potential actions, and pauses tasks scheduled within the system based on these predictions, utilizing a machine learning model and artifact repository for dynamic decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple security threats are investigated in parallel without prioritization, then all threats receive attention, but resource allocation becomes inefficient and response time increases
Solution Approach 1:
The system performs preliminary analysis of security threats by generating threat scores before full investigation begins. This preliminary scoring based on incident characteristics allows the system to prioritize which threats to investigate first, preventing time loss while ensuring all threats eventually receive appropriate attention
Solution Approach 2:
The system changes the parameter of threat evaluation by introducing dynamic threat scoring that considers multiple factors including incident type, source reputation, and system context. This parameter transformation converts raw security incidents into prioritized tasks with associated threat scores, enabling efficient resource allocation
2Productivity
If all scheduled tasks are executed regardless of security conditions, then system productivity is maintained, but security response effectiveness is reduced
Solution Approach 1:
The system dynamically adjusts task execution based on real-time security conditions. Instead of static scheduling, tasks are evaluated against current threat levels and system state, allowing the workflow to adapt between maintaining productivity and prioritizing security response based on actual conditions
Solution Approach 2:
The system implements feedback loops where security incident outcomes and threat score updates continuously inform task scheduling decisions. This feedback mechanism allows the system to learn from security events and adjust task execution priorities accordingly, balancing productivity maintenance with security response effectiveness
3Measurement precision
If threat scoring and prediction mechanisms are implemented, then security prioritization improves, but system complexity increases
Solution Approach 1:
The system segments the threat assessment process into distinct modular components: incident detection, threat score generation, prediction analysis, and workflow adjustment. This segmentation allows each component to be independently developed and maintained, reducing overall system complexity while maintaining high measurement precision through specialized sub-systems
Data Source
AI summary
An approach to workflow management in response to a detected security incident in a computer system. The approach may include an inference driven response based on prior artifacts. The inference driven response may predict the condition of the system and the outcomes of actions in response to the security incident. The predictions made by the inference drive response may be based on a machine learning model. The inference driven response may pause or prevent scheduled actions of the system based on the predictions. The inference driven response may continue to monitor the system and dynamically update its predictions for the condition of the system. In response to the updated predictions, the inference driven response may cancel or execute the previously scheduled actions of the system.