Geographically Diversified Alert Response Using Incident Embeddings
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Solution Overview
Problem
Existing cybersecurity systems face challenges in providing scalable, accurate, and reliable guided responses to security alerts due to the complexity of incident landscapes and scarcity of labeled data, limiting their effectiveness in addressing diverse cyber threats.
Innovation Solution
A geographically diversified embedding-based technique that utilizes machine learning to classify security incidents and select appropriate actions by mapping embeddings to geographical regions, enabling adaptive and efficient responses to security alerts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If fully automated systems are used to remediate security incidents, then response speed and productivity are improved, but reliability deteriorates due to high confidence thresholds being difficult to attain
Solution Approach 1:
The patent introduces a guided response system as an intermediary between fully automated remediation and manual analysis. This system uses machine learning models to generate recommended actions with confidence scores, allowing automated response while maintaining reliability through human oversight when confidence is insufficient. The intermediary layer enables automated productivity without sacrificing reliability by selectively applying automation based on confidence levels.
Solution Approach 2:
The system dynamically adjusts the confidence threshold parameter based on the specific security incident context, historical data, and model performance. By changing this parameter adaptively rather than using a fixed high threshold, the system can attain sufficient confidence for automation in many cases while maintaining reliability. The parameter change allows the system to optimize between productivity and reliability for different incident types.
2Reliability
If guided response systems are used to address security alerts, then reliability is improved through human oversight, but productivity deteriorates due to limited scalability and accuracy
Solution Approach 1:
The patent segments the security incident response process into distinct phases: automated incident classification, guided response generation, analyst review, and remediation execution. This segmentation allows machine learning models to handle routine classification and recommendation tasks, improving scalability, while human analysts focus on complex cases requiring judgment, maintaining reliability. The segmented approach enables parallel processing of multiple incidents simultaneously.
Solution Approach 2:
The system implements self-service capabilities where machine learning models automatically classify incidents, generate response recommendations, and update knowledge bases without requiring constant human intervention. This self-service automation improves productivity and scalability for routine tasks, while the system maintains reliability by designating specific cases for human review based on confidence scores and incident complexity.
3Measurement precision
If machine learning models are trained on historical security incidents to improve accuracy, then measurement precision is improved, but device complexity increases due to data scarcity and model training requirements
Solution Approach 1:
The patent merges multiple data sources and feature types into a unified incident representation model. By combining structured data (incident attributes, logs) with unstructured data (descriptions, alerts) and contextual information (historical patterns, threat intelligence), the system achieves high classification accuracy without requiring separate complex models for each data type. This merging reduces overall system complexity while improving measurement precision.
Solution Approach 2:
The system transforms security incident data from traditional tabular formats into embedding vectors in a high-dimensional semantic space. This dimensional transformation allows the machine learning models to capture nuanced patterns and relationships that are not apparent in conventional data representations, improving classification accuracy. The embedding approach also simplifies model architecture compared to handling raw structured data directly.
Data Source
AI summary
Techniques are described herein that are capable of providing a geographically diversified embedding-based guided response to a security alert. A security alert regarding an identified security incident that is associated with an entity is received. Sets of designated security incidents, which are similar to the identified security incident, may be selected from sets of historical security incidents associated with respective geographical regions based on embeddings of the identified security incident and the historical security incidents in the sets. The identified security incident is classified into selected classes using first model(s) associated with the respective geographical regions. Security actions are selected from a plurality of possible security actions using second model(s) associated with the respective geographical regions. A security recommendation regarding the security alert is generated. The security recommendation includes representations of the sets of designated security incidents, the selected classes, and/or the security actions.