NLP Intent Extraction for Continuous Cybersecurity Risk Assessment
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Solution Overview
Problem
Existing cybersecurity solutions are inadequate in performing continuous risk assessment, prone to human variation, and unable to handle variations in business language and articulation, making it challenging to maintain consistent and accurate cybersecurity assessments during the lifecycle of digital projects and assets.
Innovation Solution
Implementing a system that uses natural language processing (NLP) to monitor and analyze multiple data sources for project intents, identify cybersecurity risk levels, and enforce control measures dynamically, leveraging historical data and process mining techniques to ensure consistent and adaptive security measures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional cybersecurity assessment methods are used, then human expertise can be applied to evaluate risks, but human variation and subjectivity lead to inconsistent assessments
Solution Approach 1:
The patent replaces manual cybersecurity assessment processes with an automated NLP-based system that extracts intents from project documentation and matches them against a knowledge base of cybersecurity controls. This substitution of human mechanical assessment with automated computational analysis eliminates human variation and subjectivity, ensuring consistent and reliable assessments across different projects and assessors.
Solution Approach 2:
The system enables cybersecurity assessment to be performed automatically without requiring human expertise for each individual assessment. The NLP module autonomously analyzes project documentation, extracts relevant intents, queries the knowledge base, and generates control measure recommendations, making the assessment process self-service and eliminating dependency on human assessors.
2Productivity
If continuous monitoring of multiple data sources is implemented, then real-time risk assessment is achieved, but processing complexity and computational resources increase
Solution Approach 1:
The patent segments the cybersecurity assessment process into distinct modular components: an NLP module for intent extraction from project documentation, a knowledge base storing cybersecurity controls and requirements, and a matching engine that queries and retrieves relevant controls. This segmentation allows each component to be independently optimized and managed, reducing overall system complexity while enabling continuous monitoring and real-time assessment.
Solution Approach 2:
The NLP-based intent extraction system serves multiple functions: it analyzes various types of project documentation (requirements, designs, specifications), extracts different kinds of intents (access patterns, data flows, security requirements), and interfaces with a comprehensive knowledge base. This multi-functionality allows a single system to handle diverse assessment scenarios without requiring separate specialized tools for each data source or documentation type.
3Adaptability or versatility
If NLP-based intent extraction is used, then variations in business language are handled, but processing time and computational resources increase
Solution Approach 1:
The patent pre-populates a comprehensive knowledge base with cybersecurity controls, requirements, and best practices before the assessment process begins. This preliminary action allows the system to quickly match extracted intents against pre-defined controls without requiring complex real-time analysis or consultation during the actual assessment, significantly reducing processing time while maintaining language flexibility.
Solution Approach 2:
The system uses NLP to extract and copy relevant intent information from project documentation into a standardized format that can be directly matched against the knowledge base. This copying approach allows the system to handle variations in business language by translating diverse documentation styles into a uniform representation, enabling efficient comparison and matching without reprocessing the original varied language formats.
Data Source
AI summary
A computer-implemented method includes: monitoring data sources that document (i) one or more digital projects, or (ii) one or more information assets, wherein the data sources are subject to constant updates time elapses; extracting, for each data source and using a natural language processing (NLP) module, an intent for a digital project, or an information asset, wherein the intent characterizes how the digital project plans to access the information asset on the enterprise network, and wherein the NLP module navigates semantic differences between the data sources; identifying, for the digital project (or the information asset), a cybersecurity risk level based on consolidating the extracted intent from each of the plurality of data sources; matching the digital project (or the information asset) with a set of control measures; and subsequently causing the set of control measures to be enforced on the enterprise network for the digital project.