ML Data Control Management for Regulatory Compliance

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Solution Overview

Problem

Computing systems face significant challenges in recognizing changes to regulatory frameworks and implementing corresponding data controls, especially due to the complexity of large-scale data processing operations and the need to comply with multiple jurisdictions, leading to vulnerabilities in data security.

Innovation Solution

A method and system that utilize machine-learning models to compare dataset versions, generate feature representations, and identify affected computing systems, coordinating actions such as updating data controls or sending notifications to ensure compliance with changing regulatory frameworks.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual monitoring of regulatory framework changes is performed, then entities can stay informed about updates, but the complexity and scale of large data processing operations make it extremely challenging to recognize changes and determine affected computing systems

Engineering Contradiction:

Improvedata security complianceVSAvoiddata processing operations complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent introduces an intermediary system comprising a machine learning model that acts as a mediator between regulatory framework changes and computing systems. This intermediary automatically detects changes in regulatory frameworks, determines which computing systems are affected, and coordinates appropriate actions, thereby resolving the contradiction between maintaining reliable data security compliance and managing the complexity of large-scale data processing operations

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system enables self-service by allowing computing systems to automatically receive and implement updates regarding data control changes without manual intervention. The machine learning model autonomously monitors regulatory frameworks, identifies changes, determines affected systems, and coordinates implementation actions, freeing entities from the burden of manually tracking and implementing numerous regulatory updates across complex data processing operations

Inventive Principle:

Principle #25Self-service

2Reliability

If comprehensive monitoring of regulatory frameworks is implemented across multiple jurisdictions, then data security compliance is improved, but the time and resources required to identify and implement changes increase significantly

Engineering Contradiction:

Improveregulatory complianceVSAvoidtime to implement data control changes

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent applies preliminary action by having the machine learning model continuously and proactively monitor regulatory frameworks across multiple jurisdictions before changes affect computing systems. The system is prepared in advance to detect changes, determine affected systems, and coordinate implementation actions, thereby reducing the time loss associated with reactive compliance management

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements feedback mechanisms where the machine learning model continuously monitors regulatory frameworks, detects changes, determines affected computing systems, and coordinates actions. This closed-loop feedback system ensures timely compliance by automatically responding to regulatory changes as they occur, reducing the time and resources required to maintain compliance across multiple jurisdictions

Inventive Principle:

Principle #23Feedback

3Productivity

If automated systems are used to detect regulatory changes, then the speed and accuracy of identifying affected computing systems is improved, but the complexity of the system increases due to machine learning models

Engineering Contradiction:

Improvespeed of implementing data control updatesVSAvoidsystem architecture complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent replaces manual mechanical processes of monitoring and analyzing regulatory frameworks with an automated machine learning-based system. The machine learning model automatically detects changes in regulatory frameworks, determines affected computing systems, and coordinates implementation actions, thereby increasing productivity while the system manages its own complexity through automation rather than human intervention

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS20230306134A1Managing implementation of data controls for computing systems

Publication Date: 2023.09.28 ONETRUST LLC

AI summary

In general, various aspects of the present invention provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for managing implementation of data controls for computing systems. In various aspects, a method is provided that comprises: comparing a first version of a dataset describing a regulatory framework to a second version of the dataset to identify a change to a data control; in response: processing, using a featurization technique, a portion of the dataset to generate a feature representation of the change that comprises feature attributes representing the change; processing, using a first machine-learning model, the feature representation to generate tags representing characteristics of the change; processing, using a second machine-learning model, the tags to generate an applicable domain; identifying, based on the domain, a computing system affected by the change; and in response, coordinating an action to be performed for the computing system to address the change.