Resources

An AI environment is a set of APIs, and most of what goes wrong there is not novel. It is the ordinary API failure set arriving where teams are thinking about models.

Every argument for logging an AI system is an argument for collecting the most sensitive material in the organization into one searchable place. Both halves are true.

Residency is a question about geography. Sovereignty is a question about jurisdiction. An AI deployment can satisfy one and fail the other, and the chain here is longer than most.

An agent is a system permitted to act rather than only to answer. Everything else is a variation on that one change, and it is what makes the security question different.

Compare hosting options by what each makes your responsibility rather than by how private each sounds. The deciding question is who operates it in eighteen months.

What a readiness assessment actually hands over: the deliverable set, who owns it, what you may do with it afterwards, and what has to be true before you accept it.

How to tell whether an AI readiness assessment was worth commissioning: what to measure, what good looks like, when to check, and the measures that quietly mislead.

AI governance is how an organization decides what AI it adopts, who owns each system, what data goes in, who reviews the output, and what happens when something goes wrong.

Fourteen domains an AI governance program has to cover, what each one contains, and how to read the framework as a gap list rather than a project plan.

The order to build an AI governance program in, and why the order matters more than the list. Ten steps, and what the first ninety days realistically produce.