Business use of AI apps spans nearly every type of application, including supply chain optimization, process automation, customer service chatbots, virtual assistants, data analysis, logistics monitoring, fraud detection, competitive intelligence and more. But there are risks involved with this new technology. Take, for example:
• Pharmaceutical enterprises are trying to use their past research, trials and outcomes to train models, thereby accelerating their ability to take their next drug to the market. But what if the organization leverages an open-source model that was trained on poisoned data, leading to incorrect or misleading trial results?
No matter where you may sit on the AI adoption spectrum, it’s clear that the businesses that are embracing AI are winning a competitive edge. But it’s not as easy as plugging an AI model into your existing infrastructure stack and calling it a win. You’re adding a whole new AI stack, including the model, supply chain, plug-ins and agents—and then giving it access to sensitive internal data for both training and inference. This brings a whole new set of complexities to the security game.
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