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Vervolg op ons ISO 42001 traject, van inrichting naar aantoonbare voortgang

Door: Peter van Londen, COO WorldEmp

Drie weken geleden deelden we hoe WorldEmp ISO 42001 in de praktijk toepast. In dat artikel lieten we zien hoe we werken aan een stevig AI Management System, met aandacht voor engineering, quality assurance, training, guardrails en datagovernance.

Sindsdien hebben we in datzelfde traject opnieuw concrete stappen gezet.

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ISO 42001 in practice: from engineering to data governance

A Sustainable Alternative: Digital Knowledge Migrants

Governance and architecture

In the area of governance and architecture, we have achieved the following results:

  • We have developed a comprehensive AI Implementation High-Level Architecture & Design Playbook. This links ISO 42001 requirements to practical controls, a cloud-native architecture based on technologies such as Azure, AKS, Next.js and FastAPI, and a gated AI lifecycle delivery model.
  • We have set up clear governance structures, including RACI matrices and reference architectures for scalable, secure and ethical AI SaaS delivery.

Responsible AI Impact Assessment

  • For our generative AI SaaS environment, we have carried out a comprehensive Responsible AI Impact Assessment:
  • This assessment covers, among other things, stakeholder mapping, fairness, privacy, human oversight and risk mitigations.
  • The approach is fully aligned with the Microsoft Responsible AI Standard and ISO 42001.

Risk and incident management

  • Risk and incidents are structurally embedded in our AIMS:
  • We have created AI Impact Assessment Registers and Risk Assessment & Treatment Templates to systematically identify, evaluate and mitigate risks. This includes risks related to bias, privacy, security, reliability, operations and regulation.
  • We have implemented an Information Security Incident & Investigation Framework with AI-specific enhancements, automated reporting and root cause analysis workflows.

Our approach and principles

  • Three principles are central to our approach:
  • Human-in-the-loop oversight for high-impact decisions.
  • Continuous monitoring, incident response and improvement cycles.
  • Transparent stakeholder communication and clear disclosure of AI interactions.
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