AI adoption moved faster than enterprise governance.

Once AI spreads across an organisation, traditional governance starts encountering questions it wasn't designed to answer. Who is using which AI provider? Which models have access to enterprise information? Is confidential data leaving the organisation? And who is responsible when an autonomous agent takes an action?

Traditional IAM can establish who a user is and what enterprise applications that person can access.

AI introduces another dimension.

The same model request can carry very different risk depending on who is asking, their role, their department, the application being used, the sensitivity of the information, the destination model and the purpose of the request.

The enterprise doesn't simply need an AI dashboard. It needs a governance decision while the interaction is happening.

DataCaffé built CommandIQ as an AI Control Plane, a governance, control and observability layer for enterprise AI.

Instead of applying governance independently to every AI platform, CommandIQ evaluates the context surrounding an AI interaction.

Policy can consider the user, role, department, application, data sensitivity, model and use case before determining what should happen next.

At runtime, an interaction can move through five stages:

Identify → Classify → Decide → Route → Record

The identity behind the request is resolved. Information sensitivity and data domain can be classified. The most specific applicable policy is evaluated. Routing rules determine the appropriate model. The resulting interaction, tokens, cost and policy decision can then become part of the audit chain.

The decision itself does not have to be binary.

CommandIQ can support contextual responses such as Allow, Flag, Redact, Block or Hold for human review.

If sensitive information appears inside an otherwise legitimate request, for example, the appropriate action may be to redact the sensitive fields rather than prevent the employee from using AI altogether.

For higher-risk interactions, human approval can remain in the loop.

Identity is equally important.

Policies can become increasingly granular across:

Organisation → Department → Team → Role → User

This makes it possible to answer a fundamental enterprise AI question:

Who can do what with which AI, using what information, under which conditions?

Governance moves from something that happens after AI has been used to something that can happen inside the AI request path.

Security teams gain more precise control over sensitive information.

Technology leaders gain visibility across AI providers and models.

Finance teams can understand token consumption and cost attribution.

Managers can understand adoption patterns.

AI agents can be brought into a governance model rather than becoming invisible machine identities operating outside it.

Most importantly, governance doesn't have to become a blanket restriction on AI adoption.

The objective is not to stop employees from using AI.

It is to make AI governable enough to scale.

CommandIQ — The AI Control Plane.

Govern. Control. Observe. Optimize.