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The horizontal regimes are only half the map. Sector regulators, in finance, employment, health, and insurance, are writing their own AI rules, sometimes faster than the general law, and they stack on top of the AI Act rather than replacing it. This is where a system's real obligations often live.
A hiring model in New York, a credit model at a US bank, or a triage model in a hospital may face a sector regulator whose rule is older, more specific, and more aggressively enforced than any horizontal AI law. These instruments rarely announce themselves as "AI regulation," which is exactly why they are missed.
They also stack. A sectoral rule sits on top of the AI Act and the GDPR, so the same system carries the general duties and the sector-specific ones at once. The four below are the largest clusters; the corpus maps many more.
Banks have governed models for a decade. Supervisors expect AI to meet the same discipline: validation, documentation, monitoring, and fair-lending testing.
Hiring and workforce AI is the most heavily litigated frontier. Rules require bias audits, notice to candidates, and human review of algorithmic management.
On top of the MDR and IVDR, health regulators are setting rules for how clinical AI is validated, updated, and monitored once it is in use.
Insurance regulators are targeting the use of AI and external data in underwriting and claims, with duties to test for unfair discrimination.
A hiring model in scope of NYC Local Law 144 is also a high-risk system under the AI Act and a processor of personal data under the GDPR. The obligations combine rather than cancel, which is why mapping at the level of the obligation matters: capture the shared requirement once, and satisfy the sector rule, the horizontal law, and the data regime from one body of evidence.
Every sectoral rule sits on the same foundations. Start with the horizontal law, then layer the sector duties your footprint adds.