Every healthcare leadership team is being asked what its AI plan is. Fewer are asked the question that decides whether the plan works: is the cloud underneath it ready?
An AI system inherits everything about the environment it runs in. If nobody can say which data sits where, who can reach it, and which controls apply to it, the AI system has the same problem, at higher speed and with more people watching.
Three things to have in place
- A foundation you can trust. Access, encryption, logging, and governance that hold up when a new workload arrives, because an AI project is a new workload with unusually sensitive inputs.
- Compliance that keeps up. If every change waits on a manual review, the project will stall or go around the review. Checks built into delivery let a team move quickly and stay inside the lines.
- Data you can find and describe. Clinical and claims data arrive in many formats, including HL7 v2, C-CDA, FHIR, and X12. An AI system is only as good as the pipeline that brings those together.
On the Cloud Value Journey, the first two are the Foundation and Acceleration stages. AI belongs to the stage after them, Innovation, and it goes better when the earlier stages are finished.
Grounded, or generic
There is a second question, about the AI your own teams use to run the cloud. A general-purpose assistant can explain how to configure a storage service. It has never read your controls, it doesn’t know your asset history, and it can’t tell you when it is wrong. In healthcare, a confident wrong answer about a configuration becomes an audit finding.
The alternative is AI that is grounded in your own environment and checked by people who know when to trust it. That is how the assistants in Cloudticity Oxygen™ are built: connected to a customer’s own assets, event history, and policies, and used every day by the engineers who build them.
Designing it safely from the start
HITRUST now offers an AI Security Assessment and Certification, built on security controls written for AI systems. I served on the HITRUST AI Working Group in 2024. The practical lesson from that work is simple to state: safety, security, and compliance are far cheaper to design in than to add after a pilot has succeeded and everyone wants it in production next month.
If an AI project is on your roadmap, the useful first step is to check the foundation it will stand on. We do this work for one reason, which is to make every human on earth healthier through it. AI will help, once the ground under it holds.
Talk to a Cloud Value Architect about what your AI plans need from your cloud.
