# What does AI compliance require?

AI compliance means running models the way regulators expect systems of record to run: isolated data, logged decisions, human escalation paths, and the ability to explain any output after the fact. The practical checklist is isolation, audit trails, access control, retention policy, and provable non-training on customer data.

The hardest requirement is usually auditability: every agent action, tool grant, and model change must be reviewable months later, which has to be built into the platform rather than bolted on.

Compliance is also a speed advantage when it is infrastructure: teams that inherit it from the platform ship in weeks while competitors are still in security review.

## Related terms

- [AI agents](https://allocate.network/glossary/ai-agents.md)
- [Model weights](https://allocate.network/glossary/model-weights.md)
- [Data residency](https://allocate.network/glossary/data-residency.md)

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