Regulators expect structural control. Can you prove it?
Financial institutions increasingly deploy AI across core processes. These systems operate under multiple regulatory frameworks simultaneously. Without structural governance, compliance is not demonstrable.
AI in core processes
AI now influences the outcomes of:
Each of these systems falls under AI Act, DORA, AML directives or a combination thereof. The problem isn't that institutions lack policy — the problem is they can't prove it's being enforced.
Where it breaks down
These are the three gaps regulators find first.
No complete system overview
AI systems are developed across departments and platforms. Nobody has the full picture. During an inspection, that's the first thing that stands out.
Unclear regulatory exposure
Different regulatory frameworks apply to different systems. Without systematic classification, you don't know which obligations apply where.
Evidence that isn't reproducible
Audit evidence is manually assembled when requested. That takes weeks, is error-prone, and won't convince a regulator of structural control.
The Control Plane for financial AI
ActReady delivers a control plane that monitors regulated systems across the entire institution. The platform maintains a centralised system register, classifies regulatory obligations, enforces governance conditions and continuously records audit-ready evidence.
Not as a project. As infrastructure.
Supervision readiness
With ActReady, your institution responds to an inspection with:
- Reproducible governance evidence — always available, not assembled on request
- Clear system ownership — who is responsible for which system
- Transparent regulatory classification — which obligations apply where
- Deterministic audit reconstruction — every piece of evidence is reproducible from execution
Where are your compliance gaps?
Discuss how a control plane establishes structural governance for regulated AI systems at your institution.