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Skills application

Instruction and application
Complete

Compliance and risk management in ML​

In this skills application, you will apply what you’ve learned about compliance and risk management in machine learning to a real-world scenario. You’ll demonstrate how to align a system with regulatory requirements, design risk mitigation strategies, assign accountability, and ensure audit readiness.

Success criteria​

To successfully complete this skills application, you must:

  • Identify relevant external regulations and internal policies.
  • Map system-level risks and assign ownership.
  • Propose mitigation strategies and documentation methods.
  • Recommend practices for audit readiness and escalation.

Context​

You are working with a logistics company deploying an ML model to optimise delivery routing. The system integrates geolocation data and third-party traffic APIs. The company must meet both internal safety standards and external regulatory requirements for data use, model transparency, and system oversight.

Your task is to design a comprehensive compliance and risk oversight plan for this system.

Instructions​

Follow the prompts in the form below to complete your analysis. Completing this activity will “unlock” the solution example on the following page.

Compliance and regulatory alignment • Identify two external regulations or frameworks that apply to ML systems and one internal policy relevant to your organisation's ML practices • Briefly explain how each shapes the system's data processing, model outputs, and transparency requirements.
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Risk assessment and ownership map • List four system-level risks related to data sourcing, model operation, deployment, or integration. • For each risk, assign the appropriate owner (e.g., platform engineer, compliance officer, model owner).
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Mitigation integration and documentation • Propose three mitigation actions for identified risks (e.g., workflow gating, peer reviews, monitoring thresholds). • Explain how each mitigation will be logged or documented to support compliance and audit readiness. • Identify the key artefacts that should be created to demonstrate risk management (e.g., SOPs, sign-off forms, incident reports).
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Audit readiness and escalation workflow • Recommend two practices to maintain continuous audit readiness for this ML system (e.g., routine compliance reviews, monitoring dashboards). • Design a simple escalation plan outlining what happens when a compliance issue is detected. Include escalation levels, actions, and responsible roles.
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In progress