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AI governance
Making AI usable without losing sight of decisions, data and responsibilities.
AI governance combines technical benefits with comprehensible rules for the selection, use and operation of AI systems. The EU AI Act sets a risk-based framework for this; Copilot and other generative AI also bring with them questions about data access, permissions and Shadow AI. A viable way to start is with an AI inventory with roles, approvals, and repeatable controls - not a blanket approval or block.
Relationship to services and technologies
This page describes the company-wide governance model for AI: inventory, roles, approvals, risks and evidence. For copilot rollout and concrete readiness services, getting started leads to the solution AI Readiness / Copilot Governance.
Background
Departments test AI tools quickly and often outside of established procurement or security processes. As a result, data can end up in unclear contexts, results can be accepted without checking, or responsibilities can remain unanswered. At the same time, a lengthy individual case process prevents the desired benefit. Governance creates a reliable path between the two.
Technical context
The EU AI Act Classifies AI systems according to risks and contains different requirements depending on role and risk category. Its legal application must be assessed on a case-by-case basis. EU AI Act the regulatory classification deals with. Copilot Governance & Shadow AI focuses on the controlled introduction and use of generative AI. This hub connects both topics with the overarching control issues.
What companies need to clarify specifically
- Which AI systems, use cases, data sources and providers are in use or planned.
- Whoever is technically responsible, gives technical approval and assesses risks.
- Which uses are permitted, restricted or prohibited.
- How data access, quality assurance, human control and competencies are regulated.
- How changes, incidents and exceptions are recorded and checked in operation.
Governance in six steps
1. Inventory
Guiding question: What systems, use cases, data sources and providers are there?
Result: AI register with scope and status
2. Owner and role definition
Guiding question: Who is responsible for purpose, operation and approval?
Result: documented responsibilities
3. Risk triage
Guiding question: Which data, those affected, effects and roles are relevant?
Result: Justified depth of review and escalation
4. Release and guardrails
Guiding question: What is allowed, restricted or prohibited?
Result: Release, rules and technical controls
5. Operations and monitoring
Guiding question: How are usage, incidents and effectiveness monitored?
Result: Metrics, reviews and incident process
6. Modification and Termination
Guiding question: How are changes, new data sources and shutdown controlled?
Result: Lifecycle and verification process
Limitations and dependencies
ADIUMENTO does not provide legal advice and does not make any binding AI Act classification. Legal assessment, co-determination and professional responsibility remain with the responsible bodies. The quality of AI results and secure operation also depend on data quality, authorisations, provider configuration and implemented processes.
Orientation
Frequently asked questions
Is Copilot automatically Shadow AI?
No. Shadow AI refers to uncoordinated or uncontrolled use of AI. Even a shared service needs rules for data, permissions, use cases and operation.
Does every AI experiment have to be formally approved?
This depends on risk, data and operational context. A graduated process enables simple paths for low-risk tests and in-depth tests for sensitive use cases.
Sources for context
- Regulation (EU) 2024/1689 (AI Act) – EUR-Lex
- AI Act Service Desk – European Commission
Next sensible step
First, record AI usage, data reference and responsibilities. The existing solution leads to commercial entry into a pilot area AI Readiness / Copilot Governance on.