What does FDA-EMA · Good AI Practice (Jan 2026) cover?

Data governance, performance, life-cycle management. It applies to: Drug-development AI.

What does the Regulayer pack for FDA-EMA · Good AI Practice (Jan 2026) prove?

Maps receipts to the joint guiding principles for AI in drug development. The record is signed and tamper-evident, and anyone can verify it free, without an account.

Regulayer / The 146 laws / Pharma & life sciences

FDA-EMA · Good AI Practice (Jan 2026)

Data governance, performance, life-cycle management.

Instrument: Guiding principles, nonbindingApplies to: Drug-development AI.

What the signed record shows

Maps receipts to the joint guiding principles for AI in drug development.

The proof is a signed, tamper-evident record. Anyone can check it, free, without an account, and nothing has to leave the building to make it. Evidence, not a promise.

Citation: Principles 6, 8, 9, 2

In the same family

FDA · 21 CFR Part 11Electronic records & signatures EU GMP · Annex 11Computerised-systems integrity FDA · GMLPGood Machine Learning Practice

What to do about it

Keep the evidence this asks for, as the work happens.

A consequential AI-proposed action is checked against the human authority in force at that moment, and the decision leaves a signed record that a third party can verify independently, offline, without Regulayer. It runs inside your own environment and nothing has to leave it.

Life sciences: how the control works Or verify a real record, free →

Part of the Regulayer proof catalogue: 146 laws and standards, one sealed engine. This page is a product description, not legal advice.

What the statute requires, section by section

"Guiding Principles of Good AI Practice in Drug Development", issued jointly by the U.S. FDA and the EMA, January 2026 (10 non-binding guiding principles). This mapper addresses the principles the per-decision signed record can evidence:

Principle 6: Data governance and documentation: data source provenance, processing steps and analytical decisions are documented in a detailed, traceable and verifiable manner (in line with GxP).
Principle 8: Risk-based performance assessment: performance assessments (incl. human-AI interaction) supported by validation; a recorded performance/validation state per decision.
Principle 9: Life cycle management: risk-based quality management across the life cycle, capturing/assessing/addressing issues, with scheduled monitoring and periodic re-evaluation (e.g. data drift).
Principle 2: Risk-based approach: proportionate validation, risk mitigation and oversight based on context of use and model risk.

Taken from the Regulayer entry for this instrument, which is built against the primary text.