System of record instead of isolated lists
Inventory, risk context, legal logic, evidence, and actions stay connected per AI system instead of splitting into separate records.
AI governance system
AI governance system: steer, evidence, and approve your AI use
An AI governance system connects your AI inventory, risk assessment, obligations, approvals, runtime signals, and incidents into one traceable operating flow. This page shows what separates an operational governance system from a simple register – and how SimpleAct implements it.
In short
An AI register answers the question "Which AI systems do we have?". An AI governance system also answers: Who approved them? Which obligations apply? What happens on changes and incidents? Only then does your evidence hold up in front of auditors and authorities.
Four hard criteria
What an operational AI governance system must deliver
The real question is not whether feature names exist. The question is whether changes, reviews, and incidents create accountable follow-up work in the same system.
Reviews and approvals with real impact
Owners, reviewers, approvers, due dates, and finalization gates control when an object can actually be approved.
Runtime and change signals create work
Monitoring signals, changes, and incidents should trigger reassessment, CAPA, or review work instead of just being noted down.
Evidence and audit chain stay visible
Evidence, open points, action state, and authority packs should not disappear outside the system.
Three real flows
What AI governance work looks like in practice
Three typical flows show how a governance system works day to day – from first capturing an AI system to responding to an authority. This is how SimpleAct maps them.
Bring a new AI system to approval
A new system moves from inventory and legal logic through the audit playbook into governance. Only there does obligation mapping become defensible approval.
Create inventory and risk context
Carry over role, review cadence, and obligations from legal logic
Work through articles, gaps, and missing evidence in the audit playbook
Secure evidence, reviewers, and approvers in governance
Control model changes and reassessment
As soon as a model, data source, or operating parameter changes, work must not stop at a change note. The follow-up work needs to stay visible in the system.
Capture the change or runtime signal
Mark reassessment and review need in the system
Update owner, due date, and evidence requirement
Only approve again with refreshed evidence
Close incidents through authority response
An incident is only closed properly when severity, CAPA, reassessment, evidence, and authority response stay logically connected.
Capture the incident with context and severity
Trigger compliance gate and CAPA
Keep missing evidence and owners visible
Secure authority pack and closure status for audit
Building blocks at a glance
Modules and artifacts of a complete governance system
Operational depth does not come from individual features but from the way these building blocks connect – from legal-logic reviews to API integration.
Legal logic with review cadence and review owner
Governance with evidence register, reviewers, approvers, and FINAL gates
Audit playbook with open points, owners, due dates, and quick fixes
Incident management with CAPA, compliance gate, and authority cases
Runtime monitoring with signals, change register, and observability profiles
Assurance workflows with dataset register, bias findings, validation suites, and human oversight
API keys, webhooks, and ingestion endpoints for operational integration
FAQ
Frequently asked questions about AI governance systems
What is an AI governance system?
An AI governance system is the combination of processes, roles, and tooling a company uses to steer its AI use: capture systems, assess risks, assign obligations, document approvals, and track incidents. It creates the evidence base for the EU AI Act, GDPR, and internal policies.
How does a governance system differ from an AI register?
A register lists AI systems and their properties. A governance system connects that register with reviews, approvals, actions, and runtime signals: changes and incidents trigger traceable follow-up work instead of just being noted down.
Does the EU AI Act require an AI governance system?
The term itself does not appear in the regulation. For high-risk systems, however, the EU AI Act requires a risk management system (Art. 9), a quality management system (Art. 17), and post-market monitoring – in practice, these obligations can only be met reliably with an end-to-end governance system.
How does AI governance relate to ISO/IEC 42001?
ISO/IEC 42001 describes an AI management system (AIMS) with very similar requirements: roles, risk assessment, lifecycle controls, and continuous improvement. A well-run AI governance system provides the structure and evidence an ISO/IEC 42001 certification requires.
Make AI governance operational with SimpleAct
Start with the inventory and legal logic, then expand step by step to governance gates, runtime monitoring, and incident management – all in the same system, without scattered lists.