Control your AI before you scale it.
A control plane sits in the request path between AI and the systems AI uses. The AI can be a person in a chat assistant or an autonomous agent calling tools. In both cases a gateway enforces policy. You operate the gateway, not the model provider.
The question a regulator asks is not whether you use AI. It is whether you control it.
Governed AI is an architecture and operating model in which every AI interaction passes identity, policy, access, approval, and audit controls before the AI can reach data or take action.
An AI control plane is a gateway in the request path between AI and the systems it uses. Users and agents authenticate to it, every call is policy-checked and logged to a named person, and it runs in your own EU cloud account. You operate it, not the model provider.
Regulated firms do not get to wait for AI rules to settle. Several are in force today, and they all ask the same thing: show who did what, with which data, and prove it.
Article 50 transparency duties are in force since 2 August 2026. High-risk obligations follow: Annex III systems from 2 December 2027, Annex I from 2 August 2028. Building the controls now means later use cases inherit them instead of retrofitting.
AI vendors and cloud services count as ICT third parties. Financial entities must govern that third-party risk and keep a register of their contractual arrangements with ICT providers. This is the strongest hook for banking.
Personal and investor data needs minimization and purpose limits, and transfers outside the EEA must satisfy Chapter V. EU-only inference reduces transfer risk and keeps that data inside the boundary.
Monitoring obligations demand traceable, auditable processes. An AI step in that chain has to leave the same evidence as any other control.
These are the tests every use case runs against. Meet them once, in one foundation, and each new use case is born compliant instead of retrofitted later.
People always know when they are talking to AI. Disclosure is enforced for everyone, not left to each team.
AI assists and suggests. People decide. Approval gates sit in front of anything that changes state.
Every AI action is logged and attributable to a named person. The record survives outside the tool.
Each use case gets only the data and tools it needs. Access follows identity provider groups, nothing wider.
Spend is capped and attributed per team and per person, with a hard stop at the cap.
Regulated data stays inside your cloud boundary, in the EU. No traffic to third parties.
A use case moves up one rung only after its controls are proven in production. Autonomy is earned with evidence, not granted by default.
AI drafts and suggests. A person reviews and acts on every output.
AI completes low-risk steps within tight bounds. A person approves before anything commits.
AI runs a workflow end to end. A person monitors and can stop it at any point.
AI acts without a person in the loop. Reserved for reversible, well-proven paths.
Governance only counts if you can point at the record. This is how each requirement maps to a regulation and to a control that produces evidence.
| Requirement | Regulatory anchor | Control that produces evidence |
|---|---|---|
| Transparency | AI Act Art 50 | Disclosure and house rules enforced across the organization through the gateway. |
| Human oversight | AI Act Arts 14 and 26, GDPR Art 22 | An autonomy tier per use case. Assistants suggest, people approve. |
| Traceability | AI Act Art 12, DORA, AML records | A per-request audit record with the caller's identity, streamed to your logging and SIEM. |
| Least privilege | GDPR Arts 5 and 32, DORA | Identity provider groups drive model and tool permissions per use case. |
| Cost accountability | Management accountability | Spend caps per organization, team, and person, with a hard stop. |
| Data sovereignty | GDPR Chapter V, EBA outsourcing | Inference runs in EU regions inside your account. No data leaves the boundary. |
The pattern transfers across any environment where an AI action needs an owner, a log, and a boundary it cannot cross.
Licensed, supervised, and accountable for who used which data, for what, and at what cost. Governed AI turns that into evidence a regulator can read.
Sensitive datasets, funding conditions, and export rules that make data residency and access control non-negotiable.
Teams where every action needs an owner and a log, and where a tool that cannot prove what it did is a tool that cannot be used.
We design governed AI foundations for European regulation. What that produces, in practice:
The outcomes above are kept non-identifiable. Named references and case detail are available on request, under NDA.
An assessment reviews how you control AI today, across nine areas from the control plane to governed delivery. How we think is public, so you can read the whole picture before we talk.
An assessment works with your Risk, Security, Platform, and Architecture people. It produces documents you can use, not a summary you file away.
Remote or on site, whichever suits your teams. You work directly with the person running the assessment, not a rotating team.
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