Deploy reliable AI workload security in Amazon EKS

AI is different from traditional workloads. Prompts can vary from simple text queries to multimedia-based analyses, resulting in variable demands on GPU resources. Container ingress controllers struggle with awareness of GPU availability, so the default round-robin distribution style leaves some GPUs congested and others underutilized.

AI also relies on a complex web of distributed services and APIs that's harder to manage, with a larger attack surface that's harder to secure. AI has become an attractive target because of this complexity, and cyber criminals are using the AI models themselves as attack vectors. Techniques such as prompt injection and model manipulation bypass traditional security mechanisms to extract sensitive data from AI, and attackers may flood AI with erroneous prompts to degrade model responsiveness and drain your resources even further. Traditional Kubernetes security isn't designed to deal with these types of attacks.

To enable truly dynamic, efficient, and secure AI in Kubernetes, you need traffic management that addresses AI-specific needs and allocates workloads accordingly. This includes awareness for request complexity and GPU availability, as well as factoring in the non-linear relationship between resources and AI throughput. Container-native security controls are a must-have for protecting AI models and preventing them from becoming access points for unauthorized use and abusive tactics.