Imagine a bank known for its very secure vault. This vault, similar to the secure places we need for our workloads in Kubernetes, doesn't just keep money safe, but also holds important AI data, like model, datasets and output. Our story will explore how the security vectors used for bank vaults can help prepare against attacks on AI workloads in Kubernetes. We'll talk about various best practices in Kubernetes and tools like KubeArmor that help keep these digital vaults safe from new threats.

But, there's a twist!! The thieves who break into the bank aren't there to steal the money or data; they want to destroy and manipulate it. This twist helps us understand the unique challenges of keeping AI workloads safe, where the main risk isn't just someone stealing the data, but also ruining or deleting it. What if someone intentionally manipulates the AI training data to turn it against us, leading to an AI takeover? It is important not only to protect data from theft, but also to ensure its integrity.

We will talk about how to keep our workloads remain secure and intact against attacks, including those that seek to corrupt the very core of AI's decision-making processes.