Parallel Search Architecture for ML-Based Compliance Data Retrieval
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Solution Overview
Problem
Existing systems for proving compliance with information security standards, such as SOC 2 and ISO 27001, are burdensome, time-intensive, error-prone, and fail to scale efficiently for multiple standards, requiring tedious documentation and third-party audits.
Innovation Solution
A computer-implemented method using machine learning to augment incomplete query-embedded digital artifacts through parallel search engines, sentence transformers, and language generative models to efficiently retrieve and generate relevant compliance data, constructing a merged corpus and surfacing prioritized responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual documentation and auditing processes are used to prove compliance with information security standards, then the process is simple to implement, but it is time-intensive, labor-intensive, and fails to scale efficiently
Solution Approach 1:
The system enables self-service compliance management by allowing the ML model to automatically retrieve, analyze, and generate compliance documentation without requiring manual intervention from auditors or compliance officers. The model autonomously queries data sources, processes information, and produces compliance artifacts, eliminating the need for time-consuming manual procedures while maintaining accuracy and thoroughness in compliance verification.
2Productivity
If multiple parallel search engines are deployed to improve search performance, then the system can retrieve more relevant compliance data faster, but the device complexity increases
Solution Approach 1:
The search system is segmented into multiple specialized search engines, each optimized for specific types of compliance data or search criteria. This segmentation allows parallel processing of different search queries simultaneously, improving overall retrieval speed and relevance. Each search engine handles specific portions of the compliance data landscape, enabling the system to scale efficiently by adding more specialized engines rather than overwhelming a single monolithic search system.
3Adaptability or versatility
If manual compliance documentation is performed, then the process is easy to control and monitor, but it is error-prone and does not scale to voluminous numbers of information security standards
Solution Approach 1:
The system incorporates feedback mechanisms where the ML model continuously learns from compliance audit results, adjusting its retrieval and analysis parameters to improve accuracy over time. The model receives feedback from compliance officers and auditors about the quality and relevance of generated documentation, allowing it to refine its performance. This feedback loop ensures high reliability in compliance verification while maintaining the ability to adapt to voluminous and evolving information security standards.
Data Source
AI summary
Systems and methods for machine learning-informed response and augmentation of incomplete queries and query artifacts that include executing parallel search engines in a machine learning pipeline based on an input of a query to retrieve one or more corpora of candidate digital artifacts, constructing a merged corpus of candidate digital artifacts based on the retrieved corpora of digital artifacts, ranking each candidate digital artifact of the merged corpus of candidate digital artifacts based on a computed relevance to the input query, and returning, via a user interface, a response to the input query based on a subset of prioritized candidate digital artifacts most relevant to the input query.