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Security threats facing AI and ML

An emerging space within AI is the need to share and access more big data from semi trusting parties in order to achieve better models and insights. A good example is multiple healthcare providers sharing images and their interpretation in order to create an AI model to detect anomalies on its own. The more images the better algorithm. This scenario requires the model to access data from all healthcare providers, but assure that images cannot be accessed by each individual healthcare provider to another.

With healthcare providers requiring constant access to patient data, the fear of potential data leaks is already high and efforts to safeguard patients are driving higher security budgets across the sector. Edge computing means security along the network perimeter is decentralized, forcing enterprises to be far more vigilant of the privacy of end-users –– as well as compliant to increasingly stringent regulation.

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The HUB Vault is an ultra-secure hardware and software confidential computing platform designed to protect your most valuable legacy systems and applications. The programmable and customizable MultiCore confidential compute platform enables companies a simple, flexible, and scalable digital transformation to the cloud regardless of legacy model.

Security threats facing AI and ML

Top 5 Security Threats Facing Artificial Intelligence and Machine Learning

An emerging space within AI is the need to share and access more big data from semi trusting parties in order to achieve better models and insights. A good example is multiple healthcare providers sharing images and their interpretation in order to create an AI model to detect anomalies on its own. The more images the better algorithm. This scenario requires the model to access data from all healthcare providers, but assure that images cannot be accessed by each individual healthcare provider to another.

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