What is Shadow AI

I’ve been hearing a lot about Shadow AI recently. It was a new term to me, so I was lucky to have the chance to speak with Dr. Arash Rahnama, Head of Applied AI at Modzy, more about it.

Dr. Arash describes Shadow AI as the distance between what the data scientist develops in the lab, and the actual product the company may need.

There’s always a difference between what you test in your lab with your own data sets and scaling, and a product that you can actually use in larger-scale datasets and applications.

Like any other cybersecurity examples, these AI models are also vulnerable to attacks getting hacked or being misused.

How does this happen with Pre Trained Models in the training phase?

When you develop an AI model, it’s usually designed to perform a specific task. You train AI on a training data set, then based on what it’s coming across during training, that model learns to make predictions.

You then take that out and develop the final product, which is then run on your input data set during test time inference.

After you deploy your AI model, it is sometimes vulnerable to noise, but you can add engineered noise to the input and completely fool the predictions that the model was trained to make.

This is the field of adversarial AI.

#security testing #ai