Threat Model Update via NLP Attribute Matching
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Solution Overview
Problem
Existing threat models are resource-intensive to maintain and require significant effort to update when new threats are discovered, as they need to be reviewed across potentially thousands of applications to determine susceptibility and necessary updates.
Innovation Solution
A threat modeling tool utilizing natural language processing and machine learning to automatically identify and update threat models by comparing newly identified threats with existing models, thereby focusing resources on impacted applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual review of all existing threat models is performed when a new threat is discovered, then complete security assessment is achieved, but significant time and computational resources are consumed
Solution Approach 1:
The patent introduces an intermediary natural language processing system that acts as a mediator between new threat intelligence and existing threat models. The NLP system automatically parses threat descriptions, extracts relevant attributes, and matches them against threat model attributes, eliminating the need for manual review of all threat models while maintaining comprehensive security assessment.
Solution Approach 2:
The patent replaces the mechanical manual review process with an automated computational system. The NLP-based system automatically compares new threat attributes with existing threat model attributes using algorithmic matching, substituting human analysts and manual processes with automated text processing and comparison mechanisms.
2Reliability
If comprehensive review of all threat models is conducted to ensure security, then all vulnerabilities are identified, but processing resources are heavily consumed
Solution Approach 1:
The patent extracts only the relevant attributes from new threat descriptions using NLP techniques. Instead of processing entire threat models or conducting comprehensive reviews, the system identifies and extracts key attributes (such as affected software components, vulnerability types, and threat characteristics) and matches only those against corresponding attributes in existing threat models, significantly reducing processing requirements.
Solution Approach 2:
The patent applies partial action by performing attribute-based matching rather than complete threat model reviews. The system processes only the essential attributes needed for vulnerability identification, using a subset of the full threat model data to achieve effective security assessment with reduced computational overhead.
3Reliability
If frequent updates to threat models are performed to maintain current security posture, then security reliability is improved, but the complexity of maintenance increases
Solution Approach 1:
The patent implements self-service through automated NLP-based threat model updates. The system automatically parses new threat intelligence, extracts attributes, performs matching against existing threat models, and generates update recommendations without requiring manual intervention. This automation maintains current security posture while eliminating the complexity of manual maintenance processes.
Solution Approach 2:
The patent performs preliminary action by pre-processing and structuring threat intelligence data using NLP techniques before matching occurs. The system预先 extracts and normalizes attributes from threat descriptions, creating a ready-to-match format that simplifies the subsequent comparison process and enables rapid updates when new threats are identified.
Data Source
AI summary
A system includes a database, a memory, and a processor. The database stores data associated with a known security threat. The memory includes a threat model associated with a software application. The processor identifies, based on natural language processing of the data associated with the known security threat, one or more attributes of software susceptible to the known security threat. The processor also identifies, based on natural language processing of the threat model, one or more attributes of the software application. The processor additionally determines, based on a comparison between the one or more attributes of software susceptible to the known security threat and the one or more attributes of the software application, that the software application is susceptible to the known security threat. In response, the processor updates the threat model to reflect the susceptibility of the software application to the known security threat.