Prioritized Attack Surface Data Structure for Vulnerability Management
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Solution Overview
Problem
Monitoring and managing an attack surface for large organizations is challenging due to numerous potential vulnerability points, making it difficult to anticipate and address malicious attacks efficiently.
Innovation Solution
The system employs an 'inside-out' analysis using machine intelligence to identify critical vulnerabilities by correlating network traffic logs with threat intelligence, mapping network traffic flows, and applying a prioritization model to generate a prioritized attack surface data structure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional monitoring methods are used to track all potential vulnerability points in a large organization, then comprehensive coverage of the attack surface is achieved, but the time and resources required become prohibitively large
Solution Approach 1:
The patent segments the attack surface management process into distinct phases: data collection from multiple sources, vulnerability identification through correlation, prioritization scoring, and remediation tracking. This segmentation allows the system to handle large attack surfaces systematically without requiring exhaustive manual monitoring of every potential vulnerability point simultaneously
Solution Approach 2:
The system performs preliminary actions by proactively collecting data from asset inventories, vulnerability scanners, and threat intelligence feeds before attacks occur. By pre-identifying and scoring vulnerabilities using the prioritization model, the organization prepares security remediation in advance rather than reacting to threats after they materialize, reducing the time needed for monitoring and response
2Measurement precision
If all vulnerability points are monitored in detail, then complete security assessment is achieved, but the complexity of managing and analyzing the data increases significantly
Solution Approach 1:
The patent introduces several intermediary components that simplify complexity: a standardized data model that normalizes vulnerability data from diverse sources, a prioritization model that translates raw vulnerability data into actionable scores, and a risk acceptance workflow that mediates between security findings and business operations. These intermediaries bridge the gap between complex data collection and simple decision-making
Solution Approach 2:
The system changes parameters by transforming vulnerability data from unstructured formats into standardized structured data with consistent schemas. The prioritization model applies parameter transformations by calculating risk scores based on multiple factors (likelihood, impact, asset criticality) and converting them into a single prioritization metric that simplifies management and reporting
3Loss of information
If comprehensive vulnerability data is collected from all sources, then complete attack surface visibility is achieved, but the difficulty of drawing meaningful inferences from the data increases
Solution Approach 1:
The patent implements feedback mechanisms where the prioritization model continuously refines vulnerability scoring based on new threat intelligence and organizational context. The system provides feedback loops that update risk scores as new information becomes available, and the risk acceptance workflow provides feedback to both security teams (about accepted risks) and business units (about security requirements), making data interpretation progressively easier over time
Solution Approach 2:
The system creates simplified copies and representations of complex vulnerability data through standardized data models and prioritization scores. Instead of requiring analysts to interpret raw vulnerability feeds directly, the system generates simplified representations (prioritized vulnerability lists, risk heat maps, executive summaries) that preserve the essential information while eliminating the complexity of the underlying data
4Measurement precision
If manual analysis of vulnerability data is performed to ensure accuracy, then high precision in vulnerability identification is achieved, but the productivity and speed of attack surface management decrease
Solution Approach 1:
The patent enables self-service through automated data collection from asset inventories and vulnerability scanners, automated correlation with threat intelligence feeds, and automated prioritization scoring using machine learning models. The system serves itself by automatically updating vulnerability data, recalculating risk scores, and generating reports without requiring manual verification of each finding, while maintaining high accuracy through multiple validation layers and expert-reviewed prioritization algorithms
Data Source
AI summary
A method for managing an attack surface is provided. The method comprises obtaining network traffic logs for the domain, correlating the logs to threats, mapping a flow of network traffic between malicious indicators and host identifiers, determining an exposed set of host identifiers, determining host attributes and indicator attributes of hosts identified in the exposed set, providing the exposed set and the attributes as input to a prioritization model, receiving prioritization scores as output from the prioritization model, and generating a prioritized attack surface data structure based on the scores. An interface is configured to modify a display based on the prioritized attack surface data structure.