From the course: AI Data Strategy: Data Procurement and Storage

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Data security and compliance in AI product development: On-premise, local AI

Data security and compliance in AI product development: On-premise, local AI

From the course: AI Data Strategy: Data Procurement and Storage

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Data security and compliance in AI product development: On-premise, local AI

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- [Instructor] We've spent some time discussing how to build high-performance storage for your AI products. Now, let's shift our focus to an equally important aspect, keeping that data safe. After all, a fast AI system is useless if it puts user data at risk. Data security is more than just protecting those valuable assets. It's about maintaining the trust of your users and meeting your legal obligations. In my years of working with AI products, I've seen how a seemingly small security oversight can have major consequences. Let's look at what truly matters to secure AI systems and ensure compliance. Effective data protection involves several layers. Encryption is a critical foundation. It protects data both when it's stored and when it's being transmitted. Strong encryption protocols make your data unreadable to anyone who shouldn't have access, thus protecting it from prying eyes. Access controls and authentication add another layer of defense. Carefully manage who can see and…

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