Cloud Policy Recommendation System for Hybrid Infrastructure
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Solution Overview
Problem
Customers face challenges in defining and managing policies for their cloud infrastructures, particularly in hybrid cloud environments, where manual configuration is laborious and error-prone, and ensuring compliance across multiple infrastructures is complex.
Innovation Solution
A policy server system that uses machine learning models to predict and recommend policies based on deployment details, simplifying the policy definition process by determining relevant rule sets and action sets, and automatically converting policies to cloud-specific formats, while facilitating compliance across multiple cloud infrastructures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If customers manually configure and define policies for their cloud infrastructures, then they can ensure compliance and control over their clouds, but the process becomes laborious, error-prone, and time-consuming
Solution Approach 1:
The system enables self-service by automatically generating policy recommendations based on deployment details and analyzing existing customer policies. The machine learning model processes deployment information and autonomously formulates suitable policy sets, reducing manual configuration effort while maintaining compliance quality.
Solution Approach 2:
The policy recommendation system acts as an intermediary between deployment details and final policy configuration. It analyzes deployment information, references existing customer policies, and generates recommended policies that bridge the gap between raw deployment data and compliant policy configurations.
2Adaptability or versatility
If customers define custom policies for their specific cloud deployments, then they can address their unique requirements, but the complexity of policy management increases across multiple infrastructures
Solution Approach 1:
The system provides universal policy management by analyzing deployment details and generating appropriate policy recommendations that can be applied across different cloud infrastructures. It maintains a repository of existing customer policies and uses machine learning to adapt them to new deployments, enabling consistent policy management across multiple environments.
Solution Approach 2:
The system segments the policy management process into distinct components: deployment detail analysis, existing policy retrieval, machine learning-based recommendation generation, and policy application. This segmentation simplifies complex policy management by breaking it down into manageable, automated steps.
3Reliability
If comprehensive policy coverage is implemented across all cloud resources, then compliance and security are enhanced, but the time and resources required to define and maintain policies increase
Solution Approach 1:
The system performs preliminary action by pre-analyzing deployment details and pre-generating policy recommendations before policies need to be implemented. The machine learning model proactively formulates suitable policy sets based on deployment information, so policies are ready when needed, reducing definition time while maintaining comprehensive coverage.
Solution Approach 2:
The system uses feedback from existing customer policies and deployment outcomes to continuously improve policy recommendations. By analyzing what policies work well for similar deployments and incorporating that feedback into the machine learning model, the system efficiently generates comprehensive policy coverage without requiring extensive manual definition time.
Data Source
AI summary
An aspect of the present disclosure facilitates customers to define policies for their clouds. In one embodiment, a policy data specifying respective policies defined by customers in their corresponding clouds is maintained. Upon receiving deployment details of a cloud (e.g., their cloud) from a customer, a set of policies of potential interest to the customer is determined based on the deployment details of the cloud and the policy data. The determined set of policies is then recommended to the customer for definition in the/their cloud. According to another aspect of the present disclosure, a machine learning (ML) model is generated based on a policy data specifying respective policies defined by customers in their corresponding clouds. As such, a set of policies of potential interest to a customer is predicted based on the ML model.