ML Security Requirement Mapping for Software Integrity
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Solution Overview
Problem
Existing software development processes struggle to integrate security requirements effectively, leading to a philosophical and practical separation between business and technical user descriptions, resulting in high false-positive and false-negative security outcomes, and a lack of scalability in expert resources.
Innovation Solution
Utilize machine learning models, specifically Natural Language Processing (NLP) and deep learning techniques like Recurrent Neural Networks (RNN), to classify functional requirements and prescribe accurate, contextual, and specific security acceptance criteria during the software development cycle.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are used to classify functional requirements and prescribe security acceptance criteria, then security requirements integration is automated and accuracy is improved, but system complexity increases
Solution Approach 1:
The patent introduces machine learning models as intermediary components that bridge functional requirements and security acceptance criteria. These models act as mediators that automatically classify requirements and generate security prescriptions without direct human intervention, thereby improving accuracy while managing complexity through automation.
Solution Approach 2:
The patent replaces manual security requirement integration processes with automated machine learning-based systems. The mechanical/manual classification and prescription process is substituted with intelligent algorithms that can process and classify functional requirements automatically, reducing human effort and improving consistency.
2Productivity
If expert resources are scaled up to handle security governance, then security coverage is improved, but resource costs and management complexity increase
Solution Approach 1:
The patent enables security governance to become self-service through automated machine learning models that can independently classify functional requirements and generate security acceptance criteria without requiring extensive expert intervention. This allows the system to scale security coverage without proportionally increasing expert resource requirements.
Solution Approach 2:
The patent uses machine learning models to replicate expert security classification capabilities across multiple requirements simultaneously. Instead of requiring one expert per requirement, the system creates copies of expert-level classification能力 through trained models, enabling scalable security governance.
Data Source
AI summary
A method may include querying, using a processing unit, a project data store with a project identifier; in response to the querying, receiving a functional requirement of a project data structure stored in the project data store as associated with the project identifier; inputting, using the processing unit, the functional requirement into a trained machine learning model, the trained machine learning model configured with output nodes corresponding to a set of security concerns; after the inputting, accessing output values from the output nodes; and adding, using the processing unit, a security concern of the set of security concerns to the project data structure based on the output values.