Governance by Design
AI needs structure.
Not just performance.
The increasing use of AI creates new requirements:
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Who decides on model approvals?
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Who is responsible for agents?
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Which data may be used?
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How can a decision be made transparent and understandable?
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What regulatory frameworks apply?
Governance cannot be implemented retroactively.
It must be an integral part of the architecture.
Governance is architecture
Our platform does not integrate governance as an additional function,
but as a structural basis.
Every interaction – whether spoken or systemically triggered –
passes through a central control plane.
The following will be checked there:
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User role
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Client context
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Model release
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Tool access rights
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Valid policy version
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Documentation obligation
The execution only takes place after that.
Key governance components
Role-based access control
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Integration with existing identity systems
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Granular authorization logic
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Client and department separation
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Context-dependent limitations
Access is not granted implicitly.
It is explicitly validated.
Model governance
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Release logic for Foundation Models
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Versioned Model Guidelines
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Option for central deactivation
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Interchangeability without architectural disruption
Models are not introduced without control.
They are managed in a structured manner.
Tool and API control
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Registration of all connected systems
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Policy-based approval
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Context-specific access restriction
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Full logging
No agent directly accesses systems.
Every access attempt is validated.
Audit & Traceability
The following data is recorded for each interaction:
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User context
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Model selection
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Applied Policy
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Tools used
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Timestamp
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Result
This information is:
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unchangeable
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exportable
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SIEM-compatible
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usable for regulatory purposes
Traceability is not an option.
It's standard.
Protection of sensitive company data
A key risk of modern AI use lies in the uncontrolled flow of data.
Our platform ensures:
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Data remains in the defined cloud environment.
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No direct access to external services without a policy
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Client isolation at the registry level
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Clear data access limits
Sensitive information remains there,
where they belong.
Regulatory orientation
The platform is designed for organizations,
which are subject to regulatory supervision.
Architectural principles support requirements from:
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DORA
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FINMA
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BaFin
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FCA
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GDPR
This is not about certificates as a marketing tool.
But rather about structural argumentation skills vis-à-vis supervisory authorities and auditors.
Governance enables scaling
Without governance:
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Shadow structures are created
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Uncoordinated AI initiatives are growing
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The regulatory risk increases.
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Retrofitting will be expensive
With governance:
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AI will be implemented consistently across the entire company
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Will innovation and control be reconciled?
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This creates long-term scalability.
Control is not an obstacle to efficiency.
It is their prerequisite.
For organizations with responsibility
This platform is aimed at companies that:
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Use AI strategically
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be reviewed by the regulatory authorities
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coordinating several departments
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Aligning cloud strategies for the long term
Governance is not a project.
It is a structural foundation.
