Skip to main content

Welcome to the workshop!

Workshop
In Progress

Welcome to Governance and Compliance Simulation - Building a Regulatory-Ready ML System​

Today's icebreaker:​

Scramble for compliance

Rearrange the following letters to uncover a key ML compliance risk—and explain how regulatory compliance or governance in ML systems can help mitigate it.

I – B – S – A

Share your response in the chat!

Today's agenda:​

  • **Review:**Recap key concepts.
  • **Practice:**Building a compliance plan.
  • **Closing:**Key takeaways and next steps.

Today's learning objectives:​

  • Analyse how governance frameworks (e.g., AREA, SAFE-D) shape compliant ML data strategies and operations.
  • Apply regulatory requirements (e.g., GDPR, AI Act) to design compliant ML system practices, including data minimisation, access control, and transparency measures.
  • Create compliance artefacts (e.g., risk matrices, audit checklists) that demonstrate readiness for external audits and regulatory reviews.
  • Design escalation protocols for responding to compliance breaches in ML systems.