AWS multicloud AI workloads: secure and scale APIs for AI

Did you know 94% of organizations deploy apps across multiple environments, including a combination of public cloud instances, private data centers, and edge systems? We're living in the hybrid multicloud era, and it makes sense when you consider the benefits. In multicloud environments, businesses are no longer beholden to a specific vendor, and they can choose the services and instance types that work best for every workload they need to process, including AI.

However, multicloud AI faces unique challenges. To maintain AI's accuracy and effectiveness, organizations need to feed it a steady stream of data, which means seamlessly connecting data repositories across environments. AI workloads are also highly dynamic, ranging from simple Q&A prompts to in-depth analyses or content creation that tax GPU resources unevenly.

Additionally, the cyberthreat landscape is more nuanced because AI has become its own attack vector through techniques such as prompt injection and model jailbreaking. AI also leverages numerous API connections-again due to its data-hungry nature-which broadens the attack surface and increases the risk of AI-originated lateral movement through a network.

And we haven't yet addressed multicloud sprawl. It goes without saying that as you add more environments, more connections, and more complexity, it becomes exponentially more difficult to manage and maintain visibility over everything.