Azure AI Foundry: Security and model diversity

In the previous article, we initially unveiled Azure AI Foundry and introduced the basic concepts and core features of this platform. Today, we will further explore two key topics in Azure AI Foundry : security and model diversity. These two aspects not only determine the reliability and performance of AI applications, but also directly affect the developer experience.

#Security  : Not just a technical issue

When we talk about AI, especially large language models (LLMs), we are often attracted by their ability to create content. However, we must recognize that no matter how advanced these models are, they are still essentially machine learning-based technologies, consisting of a series of weights and biases . This means that when designing and deploying AI solutions, it is critical to ensure that their outputs are safe and meet expectations.

 The working principle of generative AI relies on probability distribution, and the parameters obtained through training predict the next most likely token after a given input . Due to this non-deterministic nature, traditional verification methods may not be applicable here.

Azure AI Foundry addresses this challenge by introducing dedicated security assessment metrics and mechanisms to ensure that AI applications are both efficient and secure.   

#Model  Diversity: From Small to Large

Another highlight of Azure AI Foundry is the support for a variety of options from small language models to large language models. 

Small language models, although with fewer parameters, perform very well on specific tasks and can run on mobile devices or edge computing environments due to their small size .

In contrast, large language models offer broader capabilities and can handle complex multimodal inputs such as text, audio, and images, but they also come with higher costs and resource requirements.

Take Microsoft’s recently released multimodal language Phi-4-multimodal-instruct  model as an example. It can simultaneously process three types of inputs — audio, text, and images — and provide text output.

Despite the relatively small number of parameters of these models, they perform well in certain application scenarios, demonstrating that even smaller language models can be used for certain tasks .

#  Multi-model integration and intelligent agents

Another concept worth noting is multi-model ensemble. In some cases, using multiple models together to solve a problem is more effective than using a single model.

For example, when building a speech recognition system, you can first use one model to convert speech to text, and then use another model to perform sentiment analysis. This approach can not only improve efficiency but also reduce costs .

Azure AI Foundry provides strong support for this multi-model collaboration, allowing developers to flexibly choose the most suitable technology stack based on actual needs.

In addition, the concept of AI agents cannot be ignored. Unlike traditional interactive AI, agents are able to complete complex tasks autonomously without human intervention. Azure AI Foundry provides the necessary tools and support for developing such agents, enabling enterprises to achieve a higher degree of automation and intelligence.