AI Control Test Evaluation for Multi-Tenant Compliance Audits

The patent introduces an intermediary AI system that acts as a mediator between organizational policies and control evaluation. The system includes policy parsers that convert natural language policies into structured representations, control evaluators that assess controls against these policies, and evidence collectors that gather supporting documentation. This intermediary layer automates the complex matching and reasoning processes, significantly improving compliance assessment efficiency while managing system complexity through modular architecture.

The patent replaces manual mechanical review processes with automated AI-based evaluation mechanisms. Instead of human reviewers manually examining controls and policies, the system uses trained models including transformer-based architectures for policy interpretation, graph neural networks for control relationship analysis, and automated reasoning engines for compliance determination. This substitution dramatically increases productivity while the modular design keeps system complexity manageable.

The patent implements partial action by introducing relevance scoring mechanisms that evaluate supporting information on a scale rather than requiring binary pass/fail decisions for all controls. The system calculates relevance scores between controls and supporting information, allowing evaluators to focus detailed analysis on high-relevance pairs while applying automated assessment to lower-relevance items. This approach maintains measurement precision for critical controls while reducing overall evaluation time through selective deep analysis.

The patent segments the compliance evaluation process into multiple independent stages: initial relevance filtering, selective detailed analysis, and aggregate compliance determination. The system divides supporting information into categories and controls into groups, evaluating them in hierarchical passes. This segmentation allows the system to achieve comprehensive coverage without processing every control-information pair at full depth simultaneously, thereby reducing total evaluation time while maintaining precision through multi-stage verification.

The patent implements universality by designing a multi-functional AI system that handles diverse control types, policy formats, and evidence sources through a unified architecture. The policy parser can process various policy representations (natural language, structured formats, regulatory frameworks), the control evaluator can assess different control categories (technical, administrative, physical), and the evidence collector can gather multiple evidence types (documents, logs, configurations). This universal approach increases productivity by eliminating the need for separate evaluation processes for each control type while managing complexity through standardized interfaces and common reasoning mechanisms.

The patent uses copying by creating structured representations and models that replicate real-world control relationships in a simplified digital format. The system generates copied versions of policies, controls, and evidence with standardized schemas that preserve essential relationships while removing extraneous complexity. These copied representations enable automated processing through graph models, knowledge graphs, and structured data formats, making control relationship analysis more detectable and measurable while maintaining fidelity to the original complex relationships.