Multicloud Scalability and Flexibility Support AI Factories

This sounds easy, right? The smartphone you may be reading this from likely has a GPU, 5G connectivity, and a passcode. You should be good to go for private security, right? Well, not quite. For now, we'll look at three points. However, know that these are only a start and more will be uncovered as you design and model the threat landscape for your AI factory.

First, let's talk about speed. When generative AI made its initial splash with ChatGPT in late 2022, we were focused on text data. However, in 2024 increasingly we see use cases around other modalities such as images, video, text, and data mixed into the flow and application layer models based on specializations. In a distributed AI factory architecture, it might not be desirable or feasible to deploy all models everywhere. It might come down to factors such as data gravity, power gravity, or compute requirements. This is where you can select high-speed network interconnect to bridge gaps and mitigate performance issues that you face when you move dependent services away from each other.

Let's also visit model theft, one of the OWASP Top 10 risks for large language models (LLMs) and generative AI apps. Any business looking to leverage generative AI to gain a competitive advantage is going to incorporate their intellectual property into the system. This might be through training their own model with corporate data or fine tuning a model. In these scenarios, just like your other business systems, your AI factory is creating value through a model you must protect. To prevent model theft in a distributed architecture, you must ensure that this model, updates to the model, and data sources that the application needs to access are encrypted and have applied access controls.

Finally, let's consider model denial of service, also on the OWASP Top 10 for LLMs and generative AI apps. As trust is gained in AI applications, their use in critical systems increases-whether that means a significant revenue-driving system for your business or critical for life sustainment such as a healthcare scenario. The ability to access the front end and inference must be designed so that any possible way in is resilient, controlled, and secured. These access scenarios might be applied to end-user access as well as from inference services back to the core AI factory.