AI-Driven Policy Generation for Multi-Cloud Governance
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Solution Overview
Problem
Existing technologies lack the capability to efficiently generate and manage cloud governance policies that are compliant with rapidly evolving security and compliance regulations in multi-cloud environments, due to the complexity and dynamic nature of cloud systems.
Innovation Solution
Implementing AI-driven policy generation within a multi-cloud governance platform using a large language model (LLM) that can interpret existing policies, generate executable compliance checks, and create and maintain resources associated with policies for cloud instances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional ML and data engineering approaches are used for policy creation, then syntactic mechanisms and word/phrase similarity can be applied, but they are insufficient for handling arbitrary nomenclature and goal/rule expressions
Solution Approach 1:
The patent replaces traditional mechanical ML approaches (regex, word similarity) with a neural network-based semantic analysis system. The neural network model processes natural language policy descriptions and compliance requirements, enabling the system to handle arbitrary nomenclature and goal expressions through semantic understanding rather than syntactic pattern matching.
Solution Approach 2:
The patent transforms the approach by changing from fixed syntactic parameters to dynamic semantic parameters. The system uses a neural network to extract meaningful features from natural language descriptions, allowing it to adapt to different nomenclatures and expressions while maintaining accurate compliance verification through learned semantic relationships.
2Reliability
If manual policy creation is used, then complex reasoning can be applied to consider architecture, use-cases, and regulations, but it is time-consuming and not scalable
Solution Approach 1:
The patent implements a system where the neural network model automatically performs the reasoning tasks previously requiring manual expert analysis. The model takes natural language policy descriptions and compliance requirements as input, and autonomously generates compliance verification logic, eliminating the need for manual policy creation while maintaining high accuracy through sophisticated semantic understanding.
Solution Approach 2:
The patent introduces a neural network-based natural language processing intermediary that bridges the gap between human-readable policy descriptions and machine-executable compliance verification logic. This intermediary automatically translates semantic meanings into structured compliance rules, enabling both high accuracy and scalability.
3Adaptability or versatility
If traditional approaches are used for comparing compliance requirements from disparate standards bodies, then no clear methodology exists, but integrating multiple standards increases complexity
Solution Approach 1:
The patent creates a universal natural language processing framework that can handle multiple compliance standards through a single unified approach. The neural network model processes policy descriptions and compliance requirements from different standards bodies using the same semantic analysis mechanisms, enabling the system to integrate multiple standards without proportionally increasing complexity.
Data Source
AI summary
In one aspect, a method of an managing policies in a multi-cloud governance platform comprising: implementing AI-driven policy generation in the multi-cloud governance platform by: providing at least one large language model (LLM) with sufficient size to have near or better than human reasoning abilities as an emergent property of the LLM; providing a plurality of cloud-computing platform dynamically updated documentations; with the LLM, interpreting an existing policy of a cloud-computing platform as provided in the plurality of cloud-computing platform dynamically updated documentations; with the by the LLM, generating executable check, for a compliance with a policy of the cloud-computing platform; and with the LLM, creating and maintaining a plurality of resources or activities associated with the policy for at least one cloud instance of the cloud-computing platform.