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Technical perspective

AI governance that works in production

Roles, evidence, and technical controls for AI systems in regulated or responsibility-critical processes.

Technical note · 1 min read
01

Governance starts with an inventory

Organisations first need to know which AI capabilities operate in which processes, which data they use, and who is accountable for production.

A central inventory connects business use, technical components, providers, risk classification, and approval status.

02

Controls must be technically effective

Policies alone do not prevent an unauthorised system action. Permissions, logging, quality tests, and approval steps must be implemented in the platform and the process.

  • Role-based access to data and capabilities
  • Versioned tests for relevant quality criteria
  • Traceable approvals and changes
  • Defined escalation and shutdown paths
03

Evidence is produced during daily operation

An auditable operation continuously collects model and prompt versions, test results, data sources, incidents, and accountable approvals. This reduces one-off effort before an audit.

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What does this perspective mean for your data and process?

We apply the architecture question to your system landscape and responsibilities.