> For the complete documentation index, see [llms.txt](https://docs.fairmath.xyz/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.fairmath.xyz/fair-math-payments/compliance.md).

# Compliance

Fair Math Payments is designed to deliver **privacy without breaking compliance**.

Unlike “black box” privacy tools that obscure all activity, Fair Math Payments introduces a **structured and auditable privacy layer**. This allows companies, financial institutions, and DAOs to implement confidentiality while still meeting **regulatory, accounting, and reporting standards**.

####  Key Principles

* **Auditable Encryption:**\
  All encrypted transactions remain **traceable** and can be decrypted by authorized parties (e.g. internal auditors or compliance officers).
* **Selective Disclosure:**\
  Privacy rules can be set so that **certain information remains private to the public**, but can still be **selectively revealed** for audits or legal requirements.
* **Regulatory Alignment:**\
  The framework is designed to support requirements like **KYC/AML reporting, tax filings, and financial audits**without forcing public exposure of all business operations.
* **Enterprise‑ready Policies:**\
  Different departments or workflows can have **different privacy levels** — e.g., payroll might be fully private, while vendor payments are semi‑transparent for tax reporting.

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**In short:** Fair Math Payments is not just a privacy layer — it’s a **compliance‑aligned privacy framework**, ensuring that businesses can protect sensitive data **and** meet all legal, accounting, and regulatory obligations.