Adaptive Configuration File Generation for Cloud Compliance
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Solution Overview
Problem
In cloud infrastructure, deploying software components requires manual configuration of YAML files, which can lead to exposure of unnecessary information or leaks of sensitive data, especially in hybrid cloud environments, due to the complexity of setting configuration knobs and unknown target environments.
Innovation Solution
A system that uses natural language processing to generate adaptive configuration files by detecting sequencing and parameter entities from a deployment declaration, aligning them with organization compliance data, and deploying the service in the cloud using a trained machine learning-based sequence model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual configuration of YAML files is used, then flexibility in deployment is maintained, but the risk of information exposure and sensitive data leaks increases
Solution Approach 1:
The patent introduces an intermediary system that acts as a mediator between the user's deployment intent and the actual configuration file generation. This intermediary automatically generates YAML configuration files based on deployment declarations, eliminating the need for manual configuration while ensuring compliance with security policies. The intermediary system translates high-level deployment intentions into secure, compliant configuration files without exposing sensitive information.
Solution Approach 2:
The system enables self-service by allowing the configuration files to be automatically generated and adjusted based on compliance policies. The configuration files are dynamically created and modified without requiring manual intervention, with the system automatically ensuring that sensitive information is protected and compliance requirements are met. This self-service approach maintains deployment flexibility while reducing security risks.
2Ease of operation
If manual configuration of YAML files is used, then control over configuration parameters is maintained, but the complexity of the deployment process increases
Solution Approach 1:
The patent inverts the traditional approach by instead of requiring users to manually configure YAML files with detailed parameters, the system generates configuration files automatically from high-level deployment declarations. This inversion simplifies the deployment process while maintaining control, as users only need to specify their deployment intentions rather than manually configuring numerous parameters.
Solution Approach 2:
The system provides a universal solution that handles multiple configuration tasks through a single automated process. The configuration file generation system can handle various deployment scenarios, compliance requirements, and parameter configurations through one unified mechanism, reducing the overall complexity of the deployment process while maintaining comprehensive control.
3Productivity
If automated configuration generation is implemented, then the deployment process is simplified, but the requirement for trained machine learning models increases system complexity
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models with compliance policies and security requirements before deployment. The models are prepared in advance with the necessary knowledge to generate compliant configuration files, which streamlines the actual deployment process. This preliminary preparation reduces the complexity during runtime while maintaining high productivity.
Solution Approach 2:
The system replaces manual mechanical configuration processes with automated machine learning-based generation. Instead of manually creating and adjusting YAML files, the machine learning model automatically generates compliant configurations based on deployment declarations. This substitution increases productivity while the complexity is managed through automation rather than manual processes.
4Reliability
If compliance alignment is performed automatically, then the risk of non-compliant deployments is reduced, but the processing time for configuration generation increases
Solution Approach 1:
The system performs preliminary action by pre-loading compliance policies and security requirements into the machine learning model before configuration generation. The model is pre-trained with compliance knowledge, allowing it to automatically generate compliant configurations without requiring time-consuming post-generation compliance checks. This preliminary preparation ensures reliability while minimizing additional processing time.
Solution Approach 2:
The compliance alignment process is integrated continuously into the configuration generation workflow rather than being a separate post-processing step. The machine learning model continuously ensures compliance throughout the generation process, maintaining reliability while avoiding delays associated with sequential compliance verification. The useful action of compliance checking continues seamlessly with the configuration generation.
Data Source
AI summary
The present invention may include an embodiment that receives a deployment declaration in a natural language. The embodiment may detect one or more sequencing entities and one or more parameter entities using trained natural language processing. The embodiment may sequence a configuration file based on the one or more sequencing entities. The embodiment may determine a plurality of configuration parameters in the sequenced configuration file. The embodiment may substitute a configuration parameter from the plurality of configuration parameters of the sequenced configuration file with the one or more parameter entities. The embodiment may align the plurality of configuration parameters of the sequenced configuration file based on organization compliance data and deploys a tuned cloud service using the sequenced configuration file.