AI Asset Boundary Analysis for Operational Resilience Gaps
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Solution Overview
Problem
Existing software development systems lack intuitive and reliable methods for selecting appropriate generative machine learning models, validating outputs for security breaches, and ensuring compliance with ethical and regulatory guidelines, leading to inefficiencies and potential system instability.
Innovation Solution
A data generation platform that uses generative AI models to dynamically evaluate machine learning prompts, validate outputs, and automate compliance with regulatory standards by identifying and remediating gaps in AI applications, ensuring security, reliability, and modularity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If AI models are used to automate compliance monitoring and gap identification, then productivity and compliance accuracy are improved, but device complexity and computational resources increase
Solution Approach 1:
The patent introduces an AI model as an intermediary component that bridges the gap between raw system data and compliance assessment results. The AI model receives system asset data, regulatory requirements, and operational data as inputs, processes them through complex algorithms, and outputs gap identifications and compliance assessments. This intermediary layer handles the computational complexity internally while presenting simplified outputs to users, thus improving productivity without exposing the full system complexity.
Solution Approach 2:
The compliance monitoring system is segmented into distinct functional modules: data collection module, AI model processing module, gap identification module, and reporting module. Each module handles specific tasks independently, allowing the system to scale and manage complexity through modular architecture. The AI model itself is presented as a discrete, replaceable component that can be updated or swapped without affecting the entire system.
2Reliability
If comprehensive regulatory requirements are enforced across all system assets, then compliance reliability is improved, but the time and resources required for monitoring increase
Solution Approach 1:
The system performs preliminary actions by continuously collecting and pre-processing system asset data, regulatory requirements, and operational data before compliance assessments are needed. The AI model is trained in advance on historical compliance data and regulatory frameworks, enabling it to rapidly assess compliance status when queried. This preliminary preparation reduces the time required for actual compliance monitoring while maintaining comprehensive coverage of regulatory requirements.
Solution Approach 2:
The compliance monitoring system operates continuously rather than through periodic batch processing. Data collection, AI model inference, and gap identification run as continuous processes that constantly update compliance status. This continuous operation eliminates idle time between assessments and ensures real-time compliance monitoring, thereby reducing total monitoring time while maintaining high reliability through constant surveillance of all system assets.
Data Source
AI summary
The systems and methods disclosed herein receives, from a computing device, operational data indicating software or hardware assets used on informational assets, and obtains a set of alphanumeric characters defining operative boundaries for expected system assets, which include a set of common attributes. Using the set of attributes, a first set of AI models determines observed system assets from the operational data, each with specific features. A second set of AI models associates each information asset with the corresponding observed system assets. For each observed system asset, a third set of AI models identifies criteria within the alphanumeric characters, compares the criteria with the asset's features to identify gaps, and generates actions to ensure the observed system asset meets the identified criteria.