Your AI has
outgrown the platform.
For the CIO or CTO whose AI has spread across clouds, models, teams and acquisitions, with one hyperscaler as the official platform and everything else outside it. Projects are stuck before production because nobody can prove control or predict the bill.
Situations where AI control gaps show up first
AI on one cloud, and everywhere else.
One hyperscaler is the official AI platform. Its guardrails, access rules and billing cover what runs there. Copilot, open-weight models on team servers, and agents built by individual teams sit outside it, each with its own rules and its own bill.
A business unit calls an open-weight model directly. Nothing checks which model, which data or which budget. The bill arrives separately, in its own format, and the audit trail stops where the cloud's own logging stops.
Puts every model call, data connection and agent under one policy, one budget and one audit trail. The official platform stays as it is.
AI that came with an acquisition.
An acquired company arrives with its own models, its own agents and its own AI vendors. Integrating the systems takes years. Governing the AI cannot wait that long.
The acquired team's agents call models on a different cloud, with different keys, under different rules. Group security cannot say which data those agents touch.
Connects the acquired estate to the same control layer without replacing its stack. Explicit grants say which agent may use which data connection, for which call.
A risk team that needs evidence.
The project is ready for production. Then the risk team asks who approved each call, on what authority, under which policy. Nobody can answer, so the project waits.
An auditor asks for the record of every call an agent made to a customer database last quarter. The answer is spread across three platforms, and none of them logged the reason a call was allowed.
Writes every grant, every policy check and the reason each call was approved or denied to a log record you can query. You build the reports your risk team needs, our advisory work maps the evidence to your controls, and your risk team signs off.
Spend nobody can predict.
Every team has its own keys and its own bill. Nobody can say what AI costs by team, model or use case, and nothing stops a runaway agent.
A team's agent loops against a hosted model overnight. Each platform bills separately, so the total shows up only when the invoices are reconciled.
Runs cost accounting in the same step as the policy checks. Set a budget per application or transaction type, and calls stop when it is hit. A dev team capped at 2 million tokens stays capped.
Not on this list? If your AI runs across more than one cloud, model or team, the same gaps are there. Outside agents are the next step, and the same layer covers them.
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