AI Governance as a Practitioner

1. 01

   Foundations

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   AI governance distinguished from AI ethics, model risk management and data governance.

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   • AI governance and the three disciplines it is mistaken for9 min
   • The vocabulary that carries legal weight9 min
   • Why the obligations attach to you as well as to the builder9 min

   Mark the remaining 3 lessons in this module complete
2. 02

   The regulatory landscape

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   EU AI Act tiers and roles, the Swiss position, and sector guidance.

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   • The architecture of the EU AI Act9 min
   • Provider, deployer, and the Article 25 role flip9 min
   • Timelines after the Digital Omnibus9 min
   • The Swiss position: no AI act, and why that changes little10 min
   • Worked example: sector guidance and the two-track problem10 min

   Mark the remaining 5 lessons in this module complete
3. 03

   Frameworks and standards

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   NIST AI RMF, ISO/IEC 42001, 23894 and 42005, and how they fit together.

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   • The NIST AI Risk Management Framework and its four functions9 min
   • ISO/IEC 42001 as a management system, not a control list9 min
   • Guidance standards: 23894 and 42005, and the word "shall"9 min
   • Worked example: what a certificate actually proves9 min

   Mark the remaining 4 lessons in this module complete
4. 04

   Governance operating model

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   Roles, RACI, the AI committee and three lines of defence.

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   • Roles and decision rights9 min
   • RACI across the AI lifecycle9 min
   • The AI committee, and what gives it teeth9 min
   • Worked example: three lines of defence applied to AI9 min

   Mark the remaining 4 lessons in this module complete
5. 05

   Inventory and risk classification

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   Intake that surfaces shadow AI, and tiering you can defend.

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   • Intake that surfaces shadow AI9 min
   • Tiering criteria, and writing the reasoning down10 min
   • Article 6 and Annex III in practice10 min
   • Worked example: defending a classification under challenge9 min

   Mark the remaining 4 lessons in this module complete
6. 06

   Impact and risk assessments

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   FRIA, DPIA, bias, explainability, robustness, privacy and security.

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   • Which assessment do you actually owe?9 min
   • Bias testing: incompatible metrics and a published calculation10 min
   • Explainability and robustness: thresholds before results9 min
   • Privacy and security in the assessment set9 min

   Mark the remaining 4 lessons in this module complete
7. 07

   Data governance for AI

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   Lineage, quality, consent and training-data rights.

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   • Article 10 read as a work programme9 min
   • Published disclosures and the datasheet discipline8 min
   • Lineage and provenance: the trail that has to exist already8 min
   • Training-data rights: what you may use, and what you must say9 min

   Mark the remaining 4 lessons in this module complete
8. 08

   Third-party and generative AI

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   Vendor due diligence, contract clauses, and generative and agentic risks.

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   • Vendor due diligence that survives contact with a real supplier10 min
   • Contract clauses: audit rights, notification and exit9 min
   • The Article 25 role flip, and what generative systems add10 min
   • Agentic risk: the OWASP ASI taxonomy applied10 min

   Mark the remaining 4 lessons in this module complete
9. 09

   Monitoring, incidents and audit

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   KPIs and KRIs, drift, the reporting clock and audit readiness.

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   • KPIs, KRIs and thresholds set before the results9 min
   • Drift, and the Article 72 post-market monitoring system9 min
   • The reporting clock: Article 3(49), Article 73, and the logs10 min
   • Audit readiness and the evidence chain9 min

   Mark the remaining 4 lessons in this module complete
10. 10

    Capstone: insurance claims triage

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    One system taken end to end, from intake to audit readiness.

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    • Claims triage, part one: from intake to controls10 min
    • Claims triage, part two: monitoring, incident, audit and retirement10 min

    Mark the remaining 2 lessons in this module complete

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