AI Compliance Matching Using Distance Matrix Security Task Alignment
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Solution Overview
Problem
The manual process of matching security recommendation tasks with regulatory compliance standards in cloud computing environments is time-consuming and resource-intensive, requiring significant user effort and computing resources.
Innovation Solution
An AI matching module using machine learning models, including sentence embeddings and generative AI, automatically matches security recommendation tasks with regulatory compliance standards by calculating a distance matrix and validating task alignment, reducing the need for manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual matching process is used to align security tasks with compliance standards, then matching accuracy can be ensured through human review, but time consumption and resource usage increase significantly
Solution Approach 1:
The patent replaces the manual mechanical matching process with an automated machine learning system. The ML model computes distance matrices between security tasks and compliance standards, automatically identifying alignments without human intervention. This substitution maintains accuracy through algorithmic precision while eliminating the time-consuming manual review process.
Solution Approach 2:
The patent transforms the matching problem into a computational parameter optimization problem. By defining distance metrics and alignment thresholds as adjustable parameters, the system can automatically determine matches based on quantitative criteria rather than qualitative human judgment, significantly reducing time while maintaining or improving accuracy.
2Adaptability or versatility
If manual matching process is used to align security tasks with compliance standards, then complex matching scenarios can be handled with human judgment, but computing resources and user effort increase significantly
Solution Approach 1:
The patent segments the complex matching problem into manageable computational components: extracting features from security tasks and compliance standards, computing distance matrices for pairwise comparisons, and applying threshold-based filtering. This segmentation allows the system to handle complex scenarios through systematic algorithmic processing rather than requiring proportional human effort.
Solution Approach 2:
The patent introduces distance matrix computation as an intermediary step between security tasks and compliance standards. This intermediary transformation converts complex semantic matching into quantitative distance measurements, enabling automated decision-making while reducing the computational burden of direct complex scenario analysis.
3Productivity
If automated ML-based matching is used to align security tasks with compliance standards, then time consumption and resource usage are reduced, but implementation complexity increases
Solution Approach 1:
The patent creates a universal ML-based matching framework that can handle multiple compliance standards and security task types through a single unified system. The distance matrix computation and threshold-based matching mechanism serve as multi-functional components that work across different domains, reducing overall system complexity despite the automated nature of the solution.
Data Source
AI summary
Techniques for matching security recommendation tasks with a regulatory compliance standard are disclosed. A regulatory compliance standard is received as input at a first Machine Learning (ML) model. Security recommendation tasks are received as input at the first ML model. A distance matrix defining a threshold of alignment that specifies a distance between the security recommendation tasks and the regulatory compliance standard is determined by the first ML model. Based on the distance matrix, identifying a predetermined number N of the security recommendation tasks that are within the threshold of alignment. A prompt including the predetermined number N of the security recommendation tasks and the regulatory compliance standard is generated. The prompt is inputted to a second ML model. Based on the prompt, the second ML model identifies a subset of the predetermined number N of the security recommendation tasks that match the regulatory compliance standard.