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Regulatory context

AI Governance Regulations

Four regimes now shape AI governance in regulated industries. Most of their obligations overlap, which means a single, well-built governance discipline can satisfy most of all four. DIG is built to be that binder.

The regimes

How DIG maps to the regimes

DIG pillarMaps to
Information ProvenanceNIST Map; EU AI Act data governance; ISO 42001 data quality.
Decision TraceabilityNIST Govern/Manage; EU AI Act record-keeping and human oversight.
Representation IntegrityConsumer-protection and disclosure duties; AI-output accuracy.
Audit ReadinessEU AI Act conformity/logging; ISO 42001 audit; TRAIGA documentation.

Frequently asked questions

Does DIG replace NIST, the EU AI Act, or ISO 42001?

No. DIG is a discipline that helps an organization meet the obligations those regimes define. It maps the four pillars to the controls each regime expects.

Why do the regulations overlap?

They target the same underlying risks, such as provenance, oversight, documentation, and auditability. Most obligations recur across regimes, so one governance program can address most of all four.

References

  1. NIST AI Risk Management Framework (AI RMF 1.0): Govern, Map, Measure, Manage. National Institute of Standards and Technology, 2023. View source ↗
  2. Information governance: the records and data lifecycle discipline (storage, retention, disposition), distinct from AI decision governance. ARMA International, Generally Accepted Recordkeeping Principles; AIIM. View source ↗
  3. EU AI Act, Regulation (EU) 2024/1689 (Official Journal of the European Union); ISO/IEC 42001:2023; Texas Responsible AI Governance Act (TRAIGA). View source ↗
  4. USPTO Trademark Reg. No. 99559923, Digital Information Governance / DIG, owner Matthew Bertram. View source ↗