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Introduction

Introduction
Complete

What if the breach came from the data?​

Your model is performing perfectly—until it isn’t. What if the breach came not from your code, but from a forgotten access setting, a misconfigured pipeline, or a malicious data tweak you never saw coming?

Machine learning systems are not like traditional software. They learn from data, adapt over time, and introduce new vulnerabilities that attackers can exploit. This unit focuses on the specific threats facing ML systems and how to address them using secure design, robust infrastructure, and strong team protocols.

Finger pointing illustration

Why does this unit matter?​

Machine learning is becoming a core part of how industries operate, but with greater impact comes greater risk. A single breach can expose sensitive data or damage your company’s reputation.

Security can’t be an afterthought. You’ll learn how to build systems that aren't just smart, but secure and trustworthy at every stage.

Learning objectives​

By the end of this unit, you will be able to:

  • Analyse vulnerabilities across the ML lifecycle, including threats to confidentiality and integrity.
  • Assess infrastructure security for local, remote, and distributed solutions.
  • Design security strategies that address risks from inception through deployment.
  • Implement security-conscious practices within ML teams to mitigate cyber threats.
  • Develop protocols for continuous security assessment and improvement.
Reflection: Security Awareness
1. Have you ever worked on or observed an ML project where security wasn't considered from the start? What were the risks?

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2. How confident are you in your ability to identify and address security issues across the ML lifecycle?

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