AI Vector Validation for Container Build Compliance
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Solution Overview
Problem
Existing container and virtual machine deployment systems face challenges in validating custom software and data, leading to potential security breaches and compliance issues due to unverified software and data handling, which can result in security breaches or monetary fines.
Innovation Solution
A system utilizing artificial intelligence (AI) with natural language processing and neural networks to assess the compliance and efficiency of container builds and virtual machine mounts by converting metadata into vector representations and scoring them for operability and efficiency, ensuring only compliant and efficient deployments are executed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional validation methods using known databases are used for container and VM validation, then validation against registered containers and images is improved, but validation of custom software and data is worsened
Solution Approach 1:
The patent introduces an AI-based intermediary system that acts as a mediator between traditional database validation and custom software validation. The AI model analyzes metadata files, converts them to vector representations, and compares them against trained models to validate custom containers and VMs that aren't in traditional databases, while still maintaining reliability for registered items.
Solution Approach 2:
The patent replaces the mechanical/database-dependent validation system with an AI-based validation mechanism. Instead of relying solely on predefined databases and manual validation rules, the system uses machine learning models trained on metadata to automatically assess the validity and security of custom containers and VMs, enabling versatile validation without sacrificing reliability.
2Adaptability or versatility
If AI-based validation is implemented for custom software and data, then adaptability and validation capability are improved, but system complexity is worsened
Solution Approach 1:
The patent segments the validation system into distinct modular components: a metadata extraction module that retrieves information from containers and VMs, a vector conversion module that transforms metadata into numerical representations, and an AI validation module that performs the actual validation. This segmentation reduces overall system complexity by making each component independent and manageable while maintaining high adaptability for custom software validation.
3Reliability
If comprehensive validation of container builds and VM mounts is performed, then security and compliance are improved, but processing time is worsened
Solution Approach 1:
The patent applies preliminary action by training the AI validation models in advance using extensive metadata from known secure and non-compliant containers and VMs. This pre-training allows the system to perform rapid validation during actual deployment without time-consuming analysis, achieving both comprehensive security checking and fast processing by shifting the time investment to the training phase rather than the validation phase.
Data Source
AI summary
An artificial intelligence (AI) platform to support optimization of container builds and virtual machine mounts in a distributed computing environment. A provisioning file is subject to natural language processing (NLP) and a corresponding vector representation of the file is created and subject to evaluation by a set of artificial neural networks (ANN). A first ANN assesses the representation of the file with respect to compliance and operability, and the second ANN selectively assesses the representation of the file with respect to provisioning efficiency. The provisioning file is selectively process based on the provisioning efficiency, with the processing directed at provisioning a container build or mounting a VM.