AI Control Layers for Automated Process Compliance
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Solution Overview
Problem
Current systems for determining process compliance using advanced computational models face challenges such as ambiguous interpretation of governance processes and excessive resource consumption, leading to inefficiencies and inaccuracies.
Innovation Solution
The system generates intermediate control layers using artificial intelligence models, compares captured data from artifacts with control objectives, and approves artifacts based on evidence thresholds, thereby streamlining the compliance determination process and reducing resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If advanced computational models are used for data analysis and automated decision-making, then the accuracy of process compliance determination is improved, but the resource consumption increases
Solution Approach 1:
The system segments the compliance determination process into multiple layers: governance processes, control objectives, artifacts, and intermediate control layers. This segmentation allows computational models to process information in manageable chunks, improving accuracy while reducing overall resource consumption by avoiding unnecessary processing of entire datasets.
Solution Approach 2:
The system performs preliminary actions by generating intermediate control layers that pre-process and structure data before final compliance determination. This preliminary organization of data reduces the computational burden during actual compliance checking, enabling more accurate analysis with lower resource consumption.
2Reliability
If manual review of artifacts is performed, then the reliability of compliance determination is improved, but the time consumption increases
Solution Approach 1:
The system implements feedback mechanisms where artifacts are reviewed against control objectives and intermediate control layers, with results fed back into the compliance determination process. This automated feedback loop maintains reliability by systematically verifying compliance while significantly reducing time consumption compared to manual review processes.
Solution Approach 2:
The system enables self-service compliance determination where the computational models autonomously evaluate artifacts against control objectives. This self-service capability maintains reliability through systematic automated verification while eliminating the time-consuming manual review process.
3Measurement precision
If comprehensive data analysis is performed, then the accuracy of compliance determination is improved, but the device complexity increases
Solution Approach 1:
The system divides comprehensive data analysis into segmented components through the layered structure of governance processes, control objectives, artifacts, and intermediate control layers. This segmentation enables accurate compliance determination by processing data through organized, manageable sections rather than attempting to analyze everything at once, thereby reducing system complexity.
Solution Approach 2:
The intermediate control layers act as intermediaries between raw data and final compliance determination. These intermediary layers simplify the analysis process by pre-organizing and structuring data, enabling comprehensive accurate analysis without increasing device complexity as the intermediary layers manage the complexity systematically.
Data Source
AI summary
Systems, computer program products, and methods are described herein for determining process compliance using advanced computational models for data analysis and automated decision-making. The present disclosure is configured to generate one or more intermediate control layers, wherein the one or more intermediate control layers comprises one or more artificial intelligence models; generate one or more control objectives, wherein each of the one or more control objectives are associated with one or more governance processes; generate, in response to the one or more control objectives, one or more manifests; receive, in response to the one or more manifests, one or more artifacts, wherein the one or more artifacts comprise data associated with the one or more governance processes; capture data from the one or more artifacts; compare the captured data from the one or more artifacts with the one or more intermediate control layers; and approve the one or more artifacts.