Regulations/FDA CSA/AI Governance
AI Governance · FDA CSA

AI Governance for FDA CSA.

This page focuses on AI governance implications inside regulated work. It shows where AI changes evidence needs while keeping quality, regulatory, data, supplier, and human accountability controls in view.

Source basis: Computer Software Assurance for Production and Quality System SoftwareUse: evidence-readinessBoundary: not legal advice
FDA CSA TRACE FDA CSA FINAL GUIDQMSR TERMINOLOGYPRODUCTION SOFTWQUALITY MANAGEME
/ AI governance

AI changes the evidence pattern, not the need for control.

FDA CSA
Control 01

AI-enabled QMS software

If AI supports quality decisions, intended use and risk drive the assurance file.

Control 02

Automation confidence

Automated tests or AI-assisted review should have fit-for-use rationale.

Control 03

Change control

Model, rule, workflow, or vendor changes need impact assessment.

Control 04

Human review

Critical quality decisions should show who reviewed outputs and what evidence they checked.

/ Adjacent controls

AI governance must connect to existing regulated systems.

not isolated
Evidence 01

Software inventory

Connect this evidence to QMS, clinical, software, supplier, data, or lifecycle governance where applicable.

Evidence 02

Intended-use statement

Connect this evidence to QMS, clinical, software, supplier, data, or lifecycle governance where applicable.

Evidence 03

Risk assessment

Connect this evidence to QMS, clinical, software, supplier, data, or lifecycle governance where applicable.

Evidence 04

Assurance plan

Connect this evidence to QMS, clinical, software, supplier, data, or lifecycle governance where applicable.

Evidence 05

Testing rationale

Connect this evidence to QMS, clinical, software, supplier, data, or lifecycle governance where applicable.

Evidence 06

Objective evidence record

Connect this evidence to QMS, clinical, software, supplier, data, or lifecycle governance where applicable.