ML Compliance Platform for Software Process Control
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Solution Overview
Problem
Online service providers face challenges in automating the identification of software processes impacted by new regulations and determining necessary controls to comply with diverse government regulations across multiple jurisdictions, leading to burdensome manual processes and potential non-compliance risks.
Innovation Solution
A computer platform utilizing machine learning models to predict and visualize software controls for mitigating compliance risks, by ingesting regulations, extracting relevant obligations, and recommending controls through a graphical user interface that illustrates the determination process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual processes are used to identify software processes impacted by regulations and determine necessary controls, then compliance accuracy can be maintained through human expertise, but the process becomes burdensome and time-consuming
Solution Approach 1:
The patent introduces an intermediary system comprising natural language processing models and machine learning algorithms that act as a mediator between regulatory texts and software processes. This intermediary automatically extracts obligations from regulations, identifies impacted software processes, and recommends controls, thereby reducing manual effort while maintaining compliance accuracy through sophisticated automated analysis
Solution Approach 2:
The patent replaces the mechanical manual process of compliance analysis with an automated computational system. Machine learning models substitute human experts in analyzing regulatory texts, mapping obligations to software processes, and determining necessary controls, significantly reducing process time while maintaining or improving accuracy through consistent automated application of compliance criteria
2Productivity
If automated systems are implemented to identify compliance requirements and recommend controls, then process efficiency and speed are improved, but system complexity increases
Solution Approach 1:
The patent segments the compliance automation system into distinct functional modules: a natural language processing module for extracting obligations from regulations, a mapping module for identifying impacted software processes, and a recommendation module for suggesting controls. This segmentation manages system complexity by organizing functions into independent, manageable components that can be developed and maintained separately
Solution Approach 2:
The patent creates a universal compliance automation platform that handles multiple regulations, jurisdictions, and software processes through a single integrated system. The machine learning models are designed to process diverse regulatory texts and identify various types of software processes, reducing the need for multiple specialized systems and managing complexity through unified multi-functional architecture
3Reliability
If comprehensive controls are implemented to address all potential compliance risks, then compliance coverage is improved, but implementation burden and cost increase
Solution Approach 1:
The patent applies local quality by tailoring compliance controls to specific software processes and their corresponding regulatory requirements. Rather than implementing uniform controls across all systems, the machine learning model identifies and recommends targeted controls for each impacted software process based on its specific functions, data handling, and regulatory context, improving compliance coverage while reducing unnecessary implementation burden
Solution Approach 2:
The patent performs preliminary action by proactively identifying software processes impacted by regulations and recommending necessary controls before compliance issues arise. The system continuously monitors regulatory changes and automatically updates compliance recommendations, allowing organizations to advance prepare and implement controls in advance of potential violations, thereby improving compliance coverage while simplifying the implementation process through advance planning
Data Source
AI summary
Methods and systems are presented for providing a computer platform that manages the impacts of government regulations on existing software processes of an online service provider. A regulation document is obtained from a government agency. The regulation document is processed, and legal obligations relevant to an online service provider are extracted from the regulation document. An ensemble machine learning model is used to recommend, for each of the legal obligations, software controls that can be implemented within one or more software processes of the online service provider to mitigate a risk of the legal obligations. The ensemble machine learning model may include an attribute-based model and a text-based model. An explainable visual interface is provided to present the recommended software controls and context that indicates to a user how the software controls are determined for the legal obligations.