This guide covers how to secure AI workloads on AWS, especially services like SageMaker, Bedrock, and Amazon Q. It highlights that default configurations often leave environments exposed, with risks like over-permissioned access and vulnerable endpoints. A core framework includes securing three layers: data, models, and artifacts. The guide also emphasizes least-privilege IAM policies, isolating workloads in VPCs, encrypting data, and continuously monitoring model behavior. A real-world “LLMjacking” case shows how attackers can abuse leaked credentials to run AI workloads at scale. The main takeaway is that AI workloads need layered, purpose-built security controls beyond traditional cloud protections.

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