Machine-Learning Cloud Code Compliance Checks Before Deployment

The system performs compliance assessment before deployment by analyzing infrastructure-as-code files against security policies. The machine learning model evaluates code patterns, configurations, and resource definitions in advance, identifying security issues before they reach production environments. This preliminary security check prevents non-compliant deployments while maintaining efficient release cycles.

The patent introduces an intermediary compliance assessment system between code submission and deployment execution. This intermediate layer includes a machine learning model that acts as a mediator, analyzing infrastructure code and providing compliance feedback without blocking the deployment pipeline. The system integrates seamlessly into existing CI/CD workflows, allowing developers to proceed with deployment while security validation occurs in parallel.

The patent replaces manual compliance checking with an automated machine learning-based system. Instead of relying on human reviewers to manually examine infrastructure code for security issues, the system uses trained models to automatically analyze code patterns, configurations, and resource definitions. This substitution maintains thorough assessment capability while reducing time consumption from hours or days to minutes or seconds.

The machine learning model evaluates multiple compliance parameters simultaneously, including security policies, best practices, and organizational standards. By changing the assessment from a sequential manual process to a parallel automated evaluation of multiple parameters, the system achieves comprehensive coverage without increasing time consumption. The model can adjust assessment depth based on risk levels, allocating more resources to critical security checks.

The compliance assessment system applies different levels of scrutiny to different parts of infrastructure code based on their security criticality. High-risk resources such as security groups, encryption keys, and access policies receive more intensive validation, while lower-risk components undergo lighter checks. This localized quality approach ensures comprehensive security coverage without uniformly increasing complexity across the entire assessment system.

The patent segments the compliance assessment process into modular components that can be independently configured and executed. The machine learning model is divided into specialized sub-models for different infrastructure types (compute, storage, networking, security). This segmentation allows the system to scale complexity only where needed, maintaining manageable system architecture while achieving broad security coverage across diverse infrastructure resources.