Contextual Cybersecurity Threat Prioritization for Alert Triage
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The increasing volume of cybersecurity detections overwhelms human experts, requiring efficient tools to quickly mitigate risks of breaches.
Innovation Solution
A cybersecurity detection prioritization service that utilizes a machine learning model to prioritize threats based on client machine contexts, assigning numerical rankings or categorizations to detections for rapid resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human experts manually assess cybersecurity detections, then assessment accuracy is maintained, but the time and resources required increase significantly
Solution Approach 1:
A machine learning model is introduced as an intermediary between cybersecurity detections and human experts. The model processes machine contexts and generates prioritization scores, serving as a mediator that filters and ranks detections before human review, thereby maintaining accuracy while reducing time loss
Solution Approach 2:
The system enables self-service by allowing the machine learning model to autonomously assess and prioritize cybersecurity detections without requiring human intervention for every case. The model independently processes contexts and generates rankings, freeing human experts to focus only on high-priority cases
2Reliability
If all cybersecurity detections are thoroughly analyzed, then detection reliability is improved, but productivity decreases due to the overwhelming volume of reports
Solution Approach 1:
The system segments the large volume of cybersecurity detections into prioritized groups based on machine context analysis. By dividing detections into priority tiers (high, medium, low), the system maintains reliable assessment of critical threats while increasing overall productivity through automated triage
Solution Approach 2:
The system changes the parameter of assessment from uniform thorough analysis to differentiated analysis based on prioritization scores. Detections are analyzed to varying depths according to their priority level, maintaining reliability for high-priority cases while improving productivity through selective analysis
3Measurement precision
If machine learning models are trained on extensive historical data, then prioritization accuracy is improved, but system complexity increases
Solution Approach 1:
The machine learning model is trained in advance on extensive historical machine context data to learn patterns of malicious versus benign behavior. This preliminary training action embeds prioritization knowledge into the model, enabling accurate prioritization without requiring complex real-time analysis infrastructure
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A cloud-based cybersecurity detection prioritization service prioritizes cybersecurity detections reported by endpoint client devices. The endpoint client devices report the cybersecurity detections to a cloud computing environment providing the cloud-based cybersecurity detection prioritization service. The endpoint client devices also report client machine contexts sampled from the endpoint client devices. The client machine contexts are compared to a cybersecurity machine contextual profile generated by a machine learning model trained using the client machine contexts sampled from the endpoint client devices. The cybersecurity detection prioritization service prioritizes the cybersecurity detections based on the cybersecurity machine contextual profile. The cloud-based cybersecurity detection prioritization service thus provides a quick ranking or categorization for queuing thousands of daily reports of viruses, hacks, and other cybersecurity detections. Prioritization allows for timely mitigations by humans of these alerts that minimize breaches.