Global AI Assurance · Regulatory assurance
AI Supervisory Intelligence
Most AI governance tooling is built for the regulated entity. This surface is built for the authority supervising a population of them: what is registered, what is high-risk, what is running on frontier models, what is unassured, and where the concentration sits.
The figures below are illustrative of a sample national landscape, published to show the architecture rather than any jurisdiction's position.
Published as reference architecture. Control weights, grading rubrics and the full control set are set per engagement.
National AI risk landscape
Illustrative sample. Not any jurisdiction's data.
14,821
AI systems registered
1,932
High-risk systems
174
Frontier-model systems
31
Critical systems
219
Unassured systems
14
Major incidents
High
Model concentration
High
Provider concentration
Moderate
Cross-border exposure
87%
Evidence freshness
74
Critical AI resilience read
Drill-down
A supervisor should be able to move from a population read to a single control record without changing systems.
Incident pattern queries
The point of a supervisory view is not to see incidents. It is to see the pattern that makes them systemic.
All incidents involving autonomous agents performing unauthorised external actions
Nine institutions, two model families, one shared tool-server pattern. The common factor is a tool server, not a model.
All incidents where the intervention did not reach the action in time
Latency, not detection, is the failure. Concentrated in long-horizon workflows with no per-action gate.
All incidents where evidence could not be produced within the supervisory window
Evidence residency and retention gaps, concentrated in entities consuming a centralised group platform.
All incidents traced to the same provider dependency
Systemic read: concentration turns a single provider event into a sector event.
AI risk can become systemic in the same way cyber, liquidity and third-party concentration risk can. A supervisor that can only see individual breaches cannot see the pattern that makes them systemic.
A supervisory view needs a framework to supervise against, expressed so a system can execute it.
Build the framework behind it