Cloud Infrastructure Compliance Assessment for Secure Deployment
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Solution Overview
Problem
Cloud infrastructure security vulnerabilities arise due to non-compliance with requirements, leading to downtime and increased latency for users.
Innovation Solution
A system utilizing machine learning to assess cloud infrastructure code for compliance, selectively deploying the infrastructure based on severity levels to ensure security, and automatically communicating minor issues for quick resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If cloud infrastructure is deployed without compliance assessment, then deployment speed is improved, but security vulnerabilities increase
Solution Approach 1:
The system performs compliance assessment before deployment by analyzing code repositories and configuration properties in advance. The machine learning model evaluates compliance indicators and severity levels prior to infrastructure deployment, preventing non-compliant code from being deployed while maintaining efficient deployment workflows.
2Measurement precision
If compliance assessment is performed manually, then assessment accuracy is improved, but time consumption increases
Solution Approach 1:
The patent replaces manual compliance assessment with an automated machine learning-based system. The machine learning model processes code repositories and configuration properties automatically, providing accurate compliance indicators without human intervention. The system handles code analysis, compliance rule matching, and severity level assessment entirely through automated machine learning processes.
Solution Approach 2:
The compliance system serves itself by automatically analyzing code repositories, retrieving configuration properties, and generating compliance assessments without requiring manual input. The system autonomously processes compliance checks and provides actionable insights, eliminating the need for manual compliance verification while maintaining high accuracy.
3Reliability
If all compliance issues must be resolved before deployment, then security is improved, but productivity decreases
Solution Approach 1:
The system applies different compliance requirements and deployment restrictions based on the severity level of identified issues. Critical compliance failures block deployment to maintain security, while lower-severity issues allow deployment with monitoring. This localized approach to compliance enforcement maintains security where needed while enabling productivity where compliance risks are acceptable.
Solution Approach 2:
The system performs partial compliance assessment by focusing on critical compliance indicators that pose security risks, rather than requiring resolution of all compliance issues. The machine learning model identifies and prioritizes the most severe compliance failures, allowing deployment to proceed when critical security requirements are met while less critical issues can be addressed separately.
Data Source
AI summary
In some implementations, a compliance system may receive, from a pipeline system, a set of properties associated with configuration of a cloud infrastructure. Additionally, the compliance system may receive, from a code repository, a set of computer code associated with the cloud infrastructure. The compliance system may provide the set of properties and the set of computer code to a machine learning model to receive a set of compliance indicators and a set of severity levels. Each compliance indicator in the set of compliance indicators being associated with a corresponding severity level in the set of severity levels. The compliance system may selectively deploy the cloud infrastructure in response to receiving the set of severity levels.