Simulated Control Evaluation Using Transformer-Based Compliance Audits
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Solution Overview
Problem
Existing systems lack efficient methods for evaluating and ensuring compliance with service controls in organizations, particularly in multi-tenant systems, to safeguard privacy and security of user data and ensure adherence to organizational policies.
Innovation Solution
A system utilizing a transformer-based neural network processes structured query inputs to evaluate service controls, extracts supporting information, and generates reports on compliance, simulating audits to identify and remediate compliance failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional control evaluation methods are used, then comprehensive security and privacy controls can be implemented, but the evaluation process becomes time-consuming and resource-intensive
Solution Approach 1:
The patent replaces manual control evaluation processes with an automated machine learning system that uses trained models to assess control compliance. The system automatically processes control descriptions, extracts supporting information, and generates compliance assessments without requiring extensive manual review, thereby maintaining high accuracy while significantly reducing evaluation time and resource consumption.
Solution Approach 2:
The system performs preliminary actions by pre-processing and storing control information, policies, and supporting documents in structured formats before actual compliance evaluation. Control descriptions are enriched with metadata, and supporting information is organized and indexed in advance, enabling the ML models to quickly retrieve and analyze relevant data during compliance assessments without time-consuming data preparation at evaluation time.
2Measurement precision
If detailed control testing is performed to ensure compliance accuracy, then reliable compliance determination is achieved, but computational resources are wasted
Solution Approach 1:
The system applies partial action by focusing computational resources only on the most relevant controls and supporting information for each compliance assessment. The ML models are trained to identify and analyze only the critical evidence needed to determine compliance, avoiding unnecessary processing of irrelevant data. This selective approach maintains high assessment accuracy while minimizing computational resource consumption.
Solution Approach 2:
The system changes parameters by dynamically adjusting the depth and scope of control testing based on risk levels, control criticality, and available evidence quality. For high-risk controls, the system performs more detailed analysis, while for lower-risk controls, it uses streamlined assessment processes. This adaptive parameter adjustment ensures accurate compliance determination for critical controls while reducing overall computational resource requirements.
3Measurement precision
If comprehensive supporting information is collected for all controls, then accurate compliance evaluation is possible, but data processing complexity increases
Solution Approach 1:
The system segments the comprehensive control evaluation process into distinct modular components: control description parsing, supporting information extraction, relevance filtering, and compliance assessment. Each component handles a specific aspect of the evaluation process independently, processing only the data relevant to its function. This segmentation reduces overall system complexity while maintaining comprehensive and accurate compliance evaluation capabilities.
Data Source
AI summary
A system evaluates controls established for an organization. The system receives a description of a set of controls and a set of constraints. The system identifies a set of data sources storing information associated with the set of controls. The system stores vector representations of information obtained from the set of data sources in a vector database. For each control from the set of controls, the system extracts supporting information related to the control from the vector database based on vector distances. The system generates a structured query input describing the control and the supporting information, and inputs the structured query input into the trained transformer-based neural network. The system processes the output sequence generated by the trained transformer-based neural network to obtain a result indicating whether the control satisfies a constraint from the set of constraints. The system evaluates the set of controls based on the results.