Cloud Application Compliance Automation with Machine Learning
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Solution Overview
Problem
Existing cloud-based applications and systems face challenges in automating compliance, with existing technologies failing to address security vulnerabilities and inefficiencies in automating compliance activities, resulting in downtime and inefficiencies in automating compliance activities.
Innovation Solution
A system utilizing machine learning models to automate compliance activities by estimating levels of effort and generating automation estimates for cloud-based applications, reducing manual intervention and resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If compliance activities are automated using machine learning models, then productivity and efficiency are improved, but device complexity increases
Solution Approach 1:
The patent introduces an intermediary compliance management system that acts as a mediator between cloud-based applications and compliance tracking systems. This system includes a machine learning model that processes compliance data, estimates effort levels, and generates automation estimates, thereby reducing the complexity burden on individual components while improving overall productivity.
Solution Approach 2:
The compliance management system is segmented into distinct functional modules: a machine learning model for effort estimation, a compliance tracker for monitoring activities, and an automation generator for creating compliance scripts. This segmentation allows each component to specialize in specific tasks, improving productivity while managing complexity through modular design.
2Loss of time
If manual compliance monitoring is performed, then device complexity remains low, but loss of time increases
Solution Approach 1:
The compliance management system implements self-service capabilities through automated compliance monitoring and effort estimation. The machine learning model automatically analyzes compliance data and generates automation estimates without requiring manual intervention, thereby reducing time loss while managing complexity through intelligent automation rather than complex procedural systems.
Solution Approach 2:
The system incorporates feedback mechanisms where the machine learning model continuously learns from compliance tracking data to improve effort estimation accuracy. This feedback loop enables the system to adapt and optimize compliance monitoring automatically, reducing time loss while maintaining manageable complexity through data-driven improvements rather than increasing system complexity.
3Reliability
If comprehensive compliance tracking is implemented, then reliability improves, but use of energy increases
Solution Approach 1:
The machine learning model dynamically adjusts processing parameters based on the characteristics of compliance data. By changing parameters such as model complexity and processing depth according to data patterns, the system maintains high reliability for compliance assurance while optimizing energy consumption by avoiding unnecessary computational overhead.
Solution Approach 2:
The system applies partial automation estimates by focusing computational resources on generating only the essential compliance automation scripts needed for specific compliance activities. Rather than comprehensively processing all possible compliance scenarios, the system selectively applies automation where most beneficial, maintaining reliability while reducing overall energy consumption.
Data Source
AI summary
In some implementations, a compliance server may receive a set of data structures associated with one or more compliance activities. The compliance server may apply a machine learning model to the set of data structures in order to generate one or more automation estimates corresponding to the one or more compliance activities. The compliance server may output the one or more automation estimates to a user device. The compliance server may receive, from the user device, an indication of a selected compliance activity from the one or more compliance activities. The compliance server may generate an automation script for the selected compliance activity in response to the indication.