Generative Output Compliance Gating for Confidential Collaboration Data
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Solution Overview
Problem
Existing systems lack effective mechanisms to enforce acceptable use policies for generative AI systems accessing confidential business information, leading to risks of unauthorized access, data leakage, and prompt injection attacks, especially as organizations scale.
Innovation Solution
A method for automated compliance enforcement using generative AI systems to construct and validate prompts against organizational policies, ensuring that all requests conform to acceptable use policies by executing compliance checks in parallel with feature requests and discarding non-compliant outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If automated compliance enforcement is implemented using generative AI systems, then policy compliance reliability is improved, but system complexity increases
Solution Approach 1:
The patent introduces a compliance enforcement module as an intermediary component that sits between the generative AI system and the policy requirements. This module automatically generates compliance prompts, validates responses against organizational policies, and enforces policy decisions without requiring manual intervention. The intermediary handles the complexity of policy interpretation and enforcement internally, presenting a simplified interface to users while maintaining high compliance reliability through automated validation and decision-making processes.
2Productivity
If manual policy enforcement is used, then system complexity is reduced, but productivity decreases due to time-consuming compliance checks
Solution Approach 1:
The system performs preliminary compliance actions by automatically generating compliance prompts and validating responses before final policy decisions are made. The compliance enforcement module pre-validates content against organizational policies, identifies potential violations in advance, and prepares compliance determinations before user interactions are finalized. This preliminary automated validation eliminates the need for time-consuming manual compliance reviews, significantly improving productivity while reducing the time required for compliance verification.
3Measurement precision
If comprehensive policy validation is performed, then measurement precision of compliance is improved, but processing time increases
Solution Approach 1:
The compliance validation process is segmented into multiple independent stages: prompt generation, response validation, policy comparison, and decision-making. Each segment handles a specific aspect of compliance validation independently, allowing for parallel processing and optimized validation at each stage. The segmentation enables comprehensive policy validation to be performed with high precision through multiple validation checks, while reducing overall processing time by eliminating redundant operations and enabling concurrent validation of different policy aspects.
Data Source
AI summary
An architecture for parallel execution of two separate generative output requests (1) a compliance request and (2) a feature request. The compliance request prompts a generative output engine to review context provided in respect of the feature request for adherence to specified policies and returns a Boolean value embedded within a specified structured format indicating compliance or noncompliance. Both requests are executed simultaneously, with the feature request response buffered until compliance is verified. If noncompliance is detected, the feature request response is discarded, and the user is notified. Alternatively, if compliance is detected, the feature request response is provided as output to the user.


