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AI Regulation

Regulatory Drift Detection

Regulatory drift detection monitors changes in laws and guidance that can make an AI system’s retrieval, outputs, or controls outdated.

Also known as: Regulation change monitoring, Legal change detection, Policy drift detection

Definition

Regulatory drift detection is the practice of identifying when relevant laws, regulations, administrative guidance, or internal policies change in a way that affects an AI system. In legal and tax settings, “drift” often means that a previously correct answer, citation, or workflow is no longer valid because the underlying source changed.

Why it matters

  • Currency: legal obligations can change faster than models and content pipelines.
  • Risk control: drift can silently create compliance exposure if not detected.
  • Traceability: supports documentation of when and why the system was updated.
  • Operational discipline: turns “stay up to date” into a measurable process.

How it works

Typical drift detection combines source monitoring with downstream triggers:

Monitor authoritative sources -> detect changes -> classify impact -> update index/prompts -> document + review

Detection signals can include: new versions of legal texts, amendments, repeal dates, new circulars, court decisions that change interpretation, or updated internal policies that constrain retrieval and disclosure.

Practical example

A tax rate or reporting threshold changes on an official site. Drift detection flags the change, triggers re-indexing of the updated text, and creates a review task for any FAQ answers that cite the old rule.

Common questions

Q: Is this the same as model drift?

A: No. Model drift is about changes in data distributions or performance. Regulatory drift is about changes in the rules the system must follow and cite.

Q: What should happen when drift is detected?

A: At minimum: update sources, re-run retrieval and answer evaluations on affected topics, and record the change in documentation/logs.

References

Regulation (EU) 2024/1689 (EU AI Act).

NIST (2023), AI Risk Management Framework (AI RMF 1.0).