Skip to main content

Skills application solution

Instruction and application
Complete

Skills application solution​

Compare your skills application output to the solution example below provided by Multiverse subject matter experts.

Solution****Vulnerability analysis

Lifecycle stageSecurity threatMapped security principle****Data ingestionMislabeled training data could lead to biased or unsafe predictions.IntegrityModel trainingExposure of model weights due to weak access controls or unencrypted storage.ConfidentialityDeploymentMisconfigured public API could lead to model extraction or unauthorised access.Service integrity and availability

Secure infrastructure and workflow design​

Recommended infrastructure:

  • Hybrid setup – Sensitive data remains on-premises to meet healthcare privacy requirements, while cloud resources support model training and deployment at scale. Workflow improvements:

  • Encrypt sensitive data and model assets at rest and in transit using secure storage buckets and HTTPS/TLS.

  • Apply RBAC (role-based access control) across training pipelines and model registries to minimise access privileges.

  • Secure the API endpoint by requiring authentication, validating inputs, and enabling rate limiting.Team protocol development

Incident response protocol:

  • Log and investigate any anomaly or breach involving patient data or model access.
  • Immediately disable the affected endpoint.
  • Notify relevant stakeholders (e.g., hospital IT leads) and begin root cause analysis.
  • Restore a previous secure model version if needed.
  • Conduct a postmortem to document lessons learned.

Continuous monitoring protocol:

  • Monitor logs weekly for unusual access patterns or API usage spikes.

  • Track model predictions for unexpected shifts that could indicate tampering.

  • Schedule quarterly security audits of infrastructure and access settings. Role-based responsibilities:

  • Data engineer: Monitor data pipeline logs and enforce validation checks.

  • Model developer: Track model behaviour and update models based on audit findings.

  • Platform engineer: Maintain access control policies and secure deployment endpoints.

Reflection​

The most critical vulnerability in this scenario was the deployment misconfiguration, as it opened the system to real-time abuse. The proposed hybrid infrastructure, access controls, and API protections directly address this risk.

Security culture also plays a key role—by establishing a routine for monitoring and encouraging team members to raise concerns early, HealthPredict can reduce the likelihood of repeat incidents and improve their incident response maturity.

What this example does well:

  • Links each vulnerability to a lifecycle stage and security principle.
  • Justifies the infrastructure recommendation with privacy, cost, and scale in mind.
  • Defines realistic protocols with clearly assigned responsibilities.

Tips for applying this skill in your role.​

  • Proactively map risks to lifecycle stages during project planning.
  • Treat deployment as a live system—review configurations regularly.
  • Clarify who owns what part of the ML system so responses are fast and accountable.

Action item: Reflection​

  • What did you do well?
  • Where could you improve?
  • How would your current team need to adapt to apply a similar approach?