Cloud Action Generation Using Generative AI and Segmented Credentials
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Solution Overview
Problem
Developing tools to interact with cloud services is resource-intensive, requiring significant development and testing time, which leads to delays in making these tools available for use.
Innovation Solution
Employing a generative artificial intelligence or natural language model to automatically generate cloud modification actions from documentation, leveraging prompt engineering techniques to differentiate between security permissions and database updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If automated generation of cloud modification actions is implemented using natural language models, then developer time is significantly reduced, but security permission management becomes more complex
Solution Approach 1:
The patent segments the credential verification process into separate evaluation functions: one for determining credentials needed to execute commands, and another for determining credentials needed to update database parameters. This segmentation allows the system to handle different security requirements for different operations independently, resolving the complexity while maintaining automated generation benefits.
Solution Approach 2:
The patent introduces an intermediary credential evaluation system that acts as a mediator between the natural language model's automated generation and the security requirements. This intermediary layer evaluates credential requirements without requiring manual developer intervention, thus reducing developer time while managing security complexity through automated mediation.
2Reliability
If comprehensive credential verification is performed for both command execution and database updates, then security is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary credential evaluation by determining the credentials needed for command execution and database updates before actually executing the actions. This preliminary assessment allows the system to verify security requirements in advance, ensuring security while enabling efficient processing by avoiding re-verification during execution.
Solution Approach 2:
The patent implements feedback mechanisms where the credential evaluation results from command execution are used to inform the database update credential requirements. This feedback loop allows the system to optimize processing by leveraging previously determined credential information, maintaining security while reducing redundant verification time.
Data Source
AI summary
Methods are provided for leveraging generative artificial intelligence to generate commands and other aspects of modification actions that can be used by users to create, delete, and/or modify virtual machines in a cloud computing environment or to interact with aspects of some other computing environment. The generation and execution of such modification actions can implicate extensive computational and other requirements and may also require the performance of multiple tasks requiring differing levels of access credential. For example, updating a database to reflect changes made to a computing environment by execution of a modification action may require a higher level of credential than performing the changes themselves. The action generation and execution methods described herein allow users with such lower-level credentials to enact such changes while also performing associated database updates or other higher-credential actions.