Copilot and office-adjacent AI
Great for knowledge work, documents, communication and standard processes. Key: role-based access, data boundaries, prompt rules and quality guardrails.
Platforms
Tools are not the strategy – but they must fit.
Platform choices without governance quickly create shadow IT, privacy risks and frustration. We categorize platforms by use cases and define guardrails that enable productive adoption.
Overview
Typical platform clusters in organizations.
LLM APIs and internal AI apps
When you build your own assistants: architecture, prompting, retrieval, logging, security requirements, operating model and responsibilities.
Governance and evidence tooling
Policies, approvals, risk assessments, documentation and training evidence: your day-to-day operating system for AI – especially in an EU AI Act context.
Selection criteria
What actually matters in platform decisions.
Security, privacy, operations
- Data flows, logging, retention
- Tenancy, permissions, IAM
- Integration with DLP, SIEM, MDM
- Operating model: support, changes, monitoring
Governance, quality, responsibility
- Roles, policies, approvals and controls
- Risk classes and evidence (AI Act/compliance)
- Quality criteria (hallucinations, sources, review)
- Enablement: training, guidelines, community
How it connects
Relevant building blocks.
AI Governance Ecosystem
To operate platforms cleanly: roles, committees, policies, controls and evidence as one coherent system.
Learn moreAI Responsibility Framework
Guardrails and decision questions for responsible adoption – especially helpful for policies and training concepts.
Learn moreServices (training/workshops)
Without capability there is no impact: role-based AI literacy, workshops and impulses for safe, productive use.
View servicesNext step
Platforms + governance = productive adoption.
In a short platform check we align on target state, use cases, data classification and the right guardrails.