In which scenario are ML models deployed on-premises?

When ML models are deployed in an airgapped scenario, it refers to a highly secure environment that is physically isolated from unsecured networks, such as the internet. This type of deployment is often utilized by organizations that handle sensitive data and require strict security measures, such as government entities, defense organizations, or companies dealing with confidential client information.

An airgapped environment prevents potential data breaches by ensuring that the system can operate without any external connections. In this situation, deploying machine learning models on-premises is essential because they cannot rely on cloud-based services or external data sources. The models must operate entirely within the secure confines of the organization's facilities, allowing for complete control over the data and maintaining compliance with stringent security protocols.

In contrast, the other scenarios involve different deployment approaches that may not guarantee the same level of security as an airgapped environment. Cloud environments and hybrid deployments often rely on internet connectivity, making them less ideal for situations requiring the highest security measures.