Automated Threat Modeling System for Enterprise Risk Assessment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional threat modeling methodologies are inadequate for assessing organizational risk, as they focus on individual applications, require security experts, are resource-intensive, and cannot scale to meet the needs of enterprises with multiple software applications, nor can they effectively model application interactions, third-party elements, or communicate risks to non-security experts.
Innovation Solution
An automated threat modeling system that uses servers and data stores to generate relational diagrams and threat reports, allowing for the visualization of threat models and interactions within a computing environment, and can be updated automatically to reflect changes, enabling non-experts to understand and manage risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional threat modeling methodologies are used to analyze individual applications, then security analysis can be performed, but the process cannot scale to meet the needs of enterprises with multiple software applications and is resource-intensive
Solution Approach 1:
The system segments the threat modeling process into automated discovery components and analysis components. The automated discovery engine separately identifies components, data flows, and threats, while the analysis engine processes this information. This segmentation allows scaling to enterprise environments without proportionally increasing resource requirements.
Solution Approach 2:
The system implements self-service through automated discovery engines that automatically survey applications, identify components, and generate threat models without requiring security experts for each individual analysis. The system serves itself by autonomously performing reconnaissance and initial threat identification.
2Measurement precision
If traditional threat modeling requires security subject-matter experts for creation, use, and maintenance, then accurate threat analysis can be achieved, but the process becomes resource-intensive and difficult to scale
Solution Approach 1:
The system introduces an automated discovery engine as an intermediary between the applications being analyzed and the security analysis process. This intermediary automatically performs reconnaissance, identifies components and data flows, and prepares structured information for analysis, reducing the burden on security experts while maintaining analysis quality.
Solution Approach 2:
The system replaces the manual mechanical process of security expert reconnaissance and component identification with an automated computational system. The automated discovery engine uses software-based methods to survey applications, identify vulnerabilities, and generate threat models, substituting human manual effort with automated processes.
3Measurement precision
If traditional threat modeling focuses on individual applications operating in isolation, then detailed application-level security analysis can be performed, but application-application interactions and organizational risk are not fully accounted for
Solution Approach 1:
The system achieves multi-functionality by designing the threat modeler to operate at multiple levels: individual application analysis and enterprise-wide portfolio analysis. The same core engine can analyze single applications in detail or aggregate results across multiple applications to assess organizational risk, providing both specialized and comprehensive views.
Solution Approach 2:
The system merges individual application threat models into a comprehensive enterprise-wide view. By combining data from multiple application analyses, the system identifies cross-application risks, shared vulnerabilities, and organizational-level threats that would be invisible when analyzing applications in isolation.
4Measurement precision
If existing vulnerability survey systems rely on recognizing previously identified vulnerability signatures, then known vulnerabilities can be detected, but the systems cannot recognize newly introduced threats or perform what-if scenarios
Solution Approach 1:
The system performs preliminary action by conducting automated discovery and reconnaissance before formal threat analysis. The discovery engine proactively surveys applications, identifies components and data flows, and prepares comprehensive information about the system architecture, enabling detection of both known and novel threats before they are exploited.
Data Source
AI summary
Automated threat modeling methods include providing one or more servers and one or more data stores communicatively coupled with the server(s). The data store(s) may include a plurality of threat model components stored therein (stored components) and a plurality of threats stored therein (stored threats), each stored threat associated through the data store(s) with at least one of the stored components. Using one or more input fields displayed on one or more computing devices communicatively coupled with at least one of the server(s), one or more inputs are received, the input(s) including access credentials associated with an existing computing environment and one or more inputs configured to initiate, using the server(s) and the access credentials, automatic generation of a relational diagram (diagram) of the existing computing environment and automatic generation of a threat report. Automated modeling systems include systems configured to carry out automated modeling of an existing computing environment.