AI without cloud compulsion: How to determine the right model for your local setup

The use of AI models in local environments has become increasingly important in recent years, especially for organizations that process sensitive data. An on-premises setup makes it possible to use AI models without cloud compulsion, increasing data security and autonomy. But choosing the right model for a local system is not an easy task. It requires an understanding of the requirements, system performance and security aspects associated with the processing of sensitive data.

First, it is crucial to clarify the specific requirements of the local setup. This includes the type of data that is processed, the required processing power, the number of users and the security standards that must be met. AI models vary widely in their complexity and their required resources. For example, a model suitable for a large company with high computing requirements would not be optimal for a small organization with limited resources.

Another aspect is security. AI systems are vulnerable to various attacks, including data poisoning and model theft. The new ISO standard 27090 provides a framework for security measures that should be taken into account when implementing AI systems. This standard describes security measures to protect AI systems from attacks and to ensure the integrity of the data. When selecting an AI model for a local setup, it is therefore important to also consider security aspects.

Another factor is the implementation of kill switch architectures. These architectures make it possible to monitor AI models in real time and deactivate them when necessary to minimise potential risks. However, implementing such architectures is challenging because they require careful planning and comprehensive security analysis. It is important to understand the risks and potential impact of such an architecture in order to make the right decision.

In summary, selecting the right AI model for an on-premises setup is a complex process that requires careful analysis of requirements, security aspects and technical capabilities. Careful planning and consideration of relevant standards and security measures enables a local AI setup to be implemented efficiently and safely. This allows organizations to leverage AI models without cloud coercion while ensuring security and control over their data.

## What this means for users The AI integration and self-hosting infrastructure in xynap enables secure and efficient use of AI models in on-premises environments without relying on cloud services.