Machine Learning PII Classification Across Multi-Cloud Deployments
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Solution Overview
Problem
The management of personally identifiable information (PII) in multi-cloud environments is lacking, with commercial cloud service providers providing limited support for compliance adherence, leading to inadequate PII protection and monitoring.
Innovation Solution
A machine learning-based approach is employed to identify PII using neural networks, which classifies data elements and interfaces with multiple cloud platforms to enforce compliance policies, utilizing a centralized repository to manage PII data and metadata across various applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If commercial cloud service providers focus on provisioning infrastructure resources, then infrastructure provisioning capability is improved, but PII protection and compliance support deteriorate
Solution Approach 1:
The patent introduces a third-party PII detection and management system that acts as an intermediary between organizations and cloud service providers. This intermediary system automatically scans, detects, and manages PII data across multi-cloud environments, providing compliance support without requiring cloud providers to change their infrastructure-focused business model.
2Ease of operation
If cloud service providers provide limited support for compliance adherence, then operational simplicity is improved, but PII management capability deteriorates
Solution Approach 1:
The patent implements automated self-service capabilities where the PII detection system automatically identifies, classifies, and manages protected data without requiring manual cloud provider intervention. The system autonomously performs compliance checks and enforcement actions, maintaining operational simplicity while improving PII management capability.
3Measurement precision
If machine learning models are used to classify PII data elements, then classification accuracy is improved, but computational complexity deteriorates
Solution Approach 1:
The patent applies machine learning models selectively rather than universally - using automated ML classification primarily for high-value or sensitive data elements where accuracy is critical, while using simpler rule-based methods for less sensitive data. This partial application of complex methods maintains classification accuracy for critical data while reducing overall computational complexity.
Data Source
AI summary
A method comprises identifying a request for data, and identifying one or more data elements that are responsive to the request for data. The one or more data elements are analyzed to classify whether the one or more data elements comprise personally identifiable information, wherein the analyzing is performed using one or more machine learning models. The method further comprises interfacing with one or more cloud platforms of a plurality of cloud platforms to transfer the one or more data elements that have been classified as comprising personally identifiable information to the one or more cloud platforms.