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Skills application

Skills application
Complete

Building secure ML systems​

In this skills application, you will apply what you’ve learned about secure infrastructure, ML-specific threats, team protocols, and secure workflow design.

Your goal is to design a more secure ML system for a healthcare company facing real-world security concerns.

Skills application illustration

Context​

You are part of the ML team at HealthPredict, a healthcare analytics company developing a predictive model to forecast hospital readmissions. The model is trained on sensitive patient data and deployed via a public-facing API that integrates with hospital systems.

Recently, your team has noticed suspicious access patterns, raising concerns about:

✨

Model inversion attacks

(where attackers try to reconstruct sensitive inputs from model outputs).

✨

Unauthorised API access

.

You’ve been asked to assess the system’s security posture and recommend improvements to protect patient data, reduce vulnerabilities, and support secure ML operations.

Success criteria​

  • Identify three relevant vulnerabilities in the current system.
  • Propose a secure infrastructure setup andworkflow improvements.
  • Design two team protocols (Incident Response & Continuous Monitoring) with clear roles.
  • Reflect on the most critical issues and the impact of recommendations on team culture.

Instructions​

1. Vulnerability analysis​

  • Identify three key vulnerabilities based on the model’s lifecycle (data ingestion, training, deployment).
  • For each, map the issue to a relevant security principle (confidentiality, integrity, availability).

2. Secure infrastructure and workflow design​

  • Recommend an infrastructure setup (Local, Cloud, or Hybrid) and justify your choice.
  • Propose at least three workflow improvements (e.g., encryption, API security, access control).

3. Team protocol development​

  • Draft an incident response protocol for breaches.
  • Draft a continuous monitoring protocol to proactively detect threats.
  • Assign responsibilities to roles (Data Engineer, ML Developer, Platform Engineer).

4. Reflection​

  • Which vulnerabilities were most critical and why?
  • How did your changes reduce risks?
  • How could team culture influence the success of these protocols?
Vulnerability analysis • Identify three key vulnerabilities based on the model's lifecycle (e.g., data ingestion, training, deployment). • For each, map the issue to a relevant security principle (e.g., confidentiality, integrity, availability).
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Secure infrastructure and workflow design • Recommend an infrastructure setup. Justify your choice based on security, governance, and cost. • Propose at least three workflow improvements (e.g., encryption, API security, access control) to address identified risks.
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Team protocol development • Draft one incident response protocol for model-related security breaches. • Draft one continuous monitoring protocol to proactively detect threats (e.g., monitoring logs, behaviour tracking). • Assign responsibilities to team roles (e.g., data engineer, model developer, platform engineer).
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Reflection • Which vulnerabilities did you find most critical and why? • How did your infrastructure and workflow changes reduce those risks? • How could team culture support—or hinder—the success of the protocols you designed?
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In progress