AI Governance: Beyond Compliance Alone
Compliance answers “do we meet the rules?”. Governance defines who decides which AI is used, how risks are managed, and how evidence persists across the lifecycle. Both belong together – especially under the EU AI Act.
Compliance vs. governance
Compliance focuses on concrete obligations (documentation, risk class, reporting). Governance describes steering: policies, roles, approvals, reviews, and audit readiness. Without governance, compliance stays fragile; without compliance, measurable evidence is missing.
Building blocks of strong AI governance
Clear ownership for owners, IT, legal, and business units – so no AI runs “in the shadows”.
Classify, approve before production, and document changes in a traceable way.
Playbooks and tamper-evident logs – aligned with high-risk and limited-risk obligations.
How SimpleAct supports governance
SimpleAct brings inventory, risk classes, checklists, and exportable reports into one place. You can back governance decisions with defensible data – instead of juggling spreadsheets and email threads.
FAQ
Do we need a separate governance framework?
Often a lean policy set plus tooling that enforces evidence is enough. SimpleAct covers the EU AI Act part structured; you add internal policies.
Where do I read about roles and audit?
See documentation on users & roles and the audit playbook – linked below.
Back governance with evidence
Start with an AI inventory and risk classification – the foundation for everything else.
Start for freePDF: AI governance checklist
Practical checkpoints for roles, approvals, and audit readiness.
Download PDFOpen-Source Framework
simpleact-ai-governance-framework
Open-source AI governance framework for the EU AI Act: roles, responsibilities, control structures, and operational governance paths.
