The AI Model Security client SDK now provides native access to scan machine learning models stored across multiple cloud storage platforms without requiring manual downloads. This enhanced capability allows you to perform security scans directly on models hosted in Amazon S3, Azure Blob Storage, Google Cloud Storage, JFrog Artifactory repositories, and GitLab Model Registry using your existing authentication credentials and access controls.

You can leverage this feature when your organization stores trained model repositories that require authenticated access, eliminating the need to manually download large model files or rely on external scanning services that may not have access to your secured storage environments. This approach is particularly valuable when working with proprietary models, models containing sensitive data, or when operating under strict data governance policies that prohibit transferring model artifacts outside your controlled infrastructure.

The native storage integration streamlines your security workflow by automatically handling credential resolution, temporary file management, and cleanup operations while maintaining the same local scanning capabilities you rely on for file-based model analysis. You benefit from reduced operational overhead and faster scan execution since the SDK can optimize download and scanning operations without intermediate storage steps. This capability enables seamless integration into CI/CD pipelines, automated security workflows, and compliance processes where model artifacts must remain within your organization's security perimeter throughout the scanning lifecycle.