ML-Driven Cloud Compliance Automation for Effort Prioritization

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Solution Overview

Problem

Cloud-based applications face security vulnerabilities due to incomplete compliance activities, leading to downtime and resource wastage in manual follow-up communications.

Innovation Solution

A system utilizing machine learning to estimate levels of effort for compliance activities, generate automation estimates, and output instructions for automated compliance tasks, thereby reducing manual intervention and resource consumption.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual follow-up communications are used for compliance activities, then security vulnerabilities can be addressed, but resource consumption and time loss increase

Engineering Contradiction:

ImprovesecurityVSAvoidresource consumption

Core Design Contradiction:

ReliabilityVSLoss of energy

Solution Approach 1:

The system enables self-service automation where compliance activities are automatically executed based on machine learning predictions. The ML model identifies which compliance tasks should be automated, and the system automatically performs them without manual intervention, thereby addressing security vulnerabilities while conserving resources.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system performs preliminary analysis using machine learning to predict which compliance activities are most likely to succeed and should be automated. This preliminary action allows the system to prioritize and automatically execute high-value compliance tasks before manual resources are consumed, optimizing both security outcomes and resource efficiency.

Inventive Principle:

Principle #10Preliminary action

2Reliability

If manual compliance monitoring is performed, then security issues can be identified, but downtime and productivity are reduced

Engineering Contradiction:

Improvesecurity monitoringVSAvoiddowntime

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The system replaces manual mechanical processes with automated machine learning-based compliance monitoring. The ML model continuously analyzes compliance data and automatically executes remediation activities, eliminating the need for manual monitoring that causes downtime and maintains productivity while ensuring security compliance.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The automated compliance monitoring system operates continuously without interruption to manual processes. The machine learning model runs constantly in the background, monitoring compliance status and automatically executing remediation activities, thereby maintaining uninterrupted productivity while ensuring continuous security monitoring.

Inventive Principle:

Principle #20Continuity of useful action

3Loss of energy

If automation is implemented for compliance activities, then resource efficiency improves, but system complexity increases

Engineering Contradiction:

Improveresource efficiencyVSAvoidsystem complexity

Core Design Contradiction:

Loss of energyVSDevice complexity

Solution Approach 1:

The machine learning model serves as an intermediary between manual compliance processes and full automation. It analyzes compliance data, predicts outcomes, and determines which activities should be automated, thereby introducing intelligent mediation that improves resource efficiency while managing system complexity through selective automation rather than blanket automation.

Inventive Principle:

Principle #24Intermediary (Mediator)

4Reliability

If comprehensive compliance checking is performed, then security is improved, but time and processing resources are consumed

Engineering Contradiction:

Improvesecurity complianceVSAvoidprocessing time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The machine learning model applies partial automation selectively to compliance activities with the highest predicted success probability and security impact. Rather than automating all compliance checks uniformly, the system identifies and automates only the most critical and promising activities, thereby improving security compliance efficiently while minimizing time and processing resource consumption.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS20250379790A1Compliance for cloud-based applications and computer systems using machine learning

Publication Date: 2025.12.11 CAPITAL ONE SERVICES LLC

AI summary

In some implementations, a compliance server may receive, from a cloud provider and a tracking system, data structures representing compliance activities and associated statistics. The compliance server may apply a machine learning model to estimate levels of effort for the compliance activities and prioritize them for automation based on the estimated levels of effort together with at least one organizational factor or a due date. For a selected compliance activity, the compliance server may generate an automation script derived from historical command data and transmit the script to the cloud provider for execution. The compliance server may update a compliance status record to indicate completion and may generate visual representations of activities, automation estimates, staffing estimates, and communication links to support efficient compliance management. These features provide specific improvements to computer-based compliance systems by enabling automated, dynamic, and resource-efficient remediation of cloud compliance activities.