Feature Request Risk Detection for Early SDLC Security Screening
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Solution Overview
Problem
Existing software development processes fail to effectively address non-functional aspects such as security and compliance risks during the early phases of the Software Development Lifecycle (SDLC), leading to potential materialization of risks that require significant rework and increased costs.
Innovation Solution
A system and method for detecting potential security risks in feature requests by collecting, normalizing, and analyzing feature requests using machine learning and manually defined rules, with real-time risk mitigation capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If non-functional aspects such as security and compliance are addressed during early phases of SDLC, then risk mitigation effectiveness is improved, but detection capability of security risks in feature requests deteriorates due to lack of automated tools
Solution Approach 1:
The system performs preliminary analysis of feature requests to identify security risks before development begins. The analyzer examines feature requests against stored security patterns and rules, flagging potential issues in the requirements phase so they can be addressed before implementation, thereby improving risk mitigation effectiveness while maintaining automated detection capability
Solution Approach 2:
The system introduces an intermediary analyzer component that bridges feature request documentation and security assessment. This intermediary automatically evaluates feature requests against security criteria without requiring manual security expertise at the early stage, thus improving both detection capability and risk mitigation effectiveness
2Reliability
If feature requests are analyzed using multiple providers with different formats, then comprehensive risk assessment is improved, but system complexity deteriorates due to format normalization requirements
Solution Approach 1:
The system implements a universal analyzer that can process feature requests from multiple providers with different formats. The analyzer is designed to handle various input formats and normalize them against a common set of security patterns, enabling comprehensive risk assessment across diverse sources without requiring separate analysis systems for each provider
Solution Approach 2:
The system changes the parameter of format handling by using pattern-matching and normalization techniques. Instead of requiring each provider to use a specific format, the analyzer transforms various input formats into a standardized internal representation for security analysis, reducing system complexity while maintaining comprehensive assessment capability
3Reliability
If feature requests are analyzed in real-time, then risk mitigation timing is improved, but processing speed deteriorates due to comprehensive analysis requirements
Solution Approach 1:
The system applies partial analysis by focusing on the most critical security patterns and high-risk features first. The analyzer identifies and prioritizes security concerns based on feature request characteristics, performing comprehensive analysis only on flagged items while providing rapid initial assessment for lower-risk features, thus maintaining real-time capability without sacrificing thoroughness where needed
Data Source
AI summary
A system and method for detecting feature requests that pose a potential security risk, including: collecting the feature requests from providers; and analyzing the feature requests to assess whether features requested in the feature requests will pose a security risk to the system once created.