Less-developed security

Security in the hyperscaler world is mature because the administrative ecosystem is mature. AWS, Microsoft, and Google have spent years building deeply integrated identity systems, key management services, logging tools, policy engines, compliance programs, network controls, vulnerability management capabilities, and security monitoring services. Enterprises still misconfigure these services all the time, but the building blocks are well known and widely understood.

With neoclouds, security administration may require more direct enterprise ownership. Some providers have strong security capabilities and mature operational practices. Others are still building out the kinds of enterprise-grade controls large organizations expect from the hyperscalers. That means administrators cannot assume that identity federation, privileged access controls, audit logging, encryption, network segmentation, and compliance reporting will behave in familiar ways.

This matters because AI workloads often involve some of the most valuable data an enterprise owns. Training sets, fine-tuning data, prompts, embeddings, model weights, vector databases, and inference outputs may contain intellectual property, customer data, regulated information, or confidential business logic. If an enterprise is using proprietary operational data to fine-tune a model, the administrative stakes are higher than simply spinning up remote compute.