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Async review

Instruction and application
In Progress

Recap core topics:​

  • Unit 1: Security Fundamentals in Machine Learning

Unit 1: Security Fundamentals in Machine Learning​

In Unit 1, you explored…

  • Core security principles – Confidentiality, integrity, authentication, non-repudiation, and service integrity in the context of ML workflows.
  • Lifecycle threats – Common vulnerabilities across the ML lifecycle, including data poisoning, model inversion, insecure APIs, and audit gaps.
  • Infrastructure decisions – Trade-offs between local, cloud, and hybrid setups in terms of control, compliance, scalability, and risk exposure.
  • Secure workflow design – Techniques such as threat modelling, least privilege, encryption, and monitoring embedded into ML pipelines.
  • Team protocols and culture – Role-based responsibilities, incident response practices, and a culture of continuous security improvement.

Five principles shape every stage of a secure ML system​

PrincipleFocus areaML exampleConfidentialityPreventing unauthorised access to data/modelsEncrypting training data and protecting model outputsIntegrityEnsuring correctness of data and models Detecting tampering in training labels or model weightsAuthenticationVerifying user/system identity Using API keys and MFA to restrict model accessNon-repudiation Enabling traceability of actions Logging model updates and access eventsService integrity Defending runtime model behavior Blocking adversarial inputs and monitoring for drift

Threats across the ML lifecycle: Where can things go wrong?​

  • Data poisoning: Malicious input during training corrupts model behaviour.

  • Model inversion: Attackers reconstruct training data from outputs.

  • Adversarial inputs: Carefully crafted inputs trick deployed models.

  • API misuse: Open or unauthenticated endpoints expose model logic or data.Secure ML practices

  • Encrypt sensitive assets.

  • Apply role-based access controls.

  • Use secure APIs with rate limiting.

  • Log access and model events.

Who protects what in your ML system?

Security in ML systems is a shared responsibility across roles:

  • Data scientists: Monitor model behaviour and validate training data.
  • Engineers: Secure data pipelines and configure access controls.
  • Platform admins: Manage infrastructure and enforce CI/CD security.

Action item: Quick reflection - What matters most for your role?​

Which security concept feels most critical for your role—and why?

Share your response in the chat!