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Introduction to chapter 3

Welcome to the turbulence zone: risk management.

So far, we have seen that GUS (the MagicFridge AI) was a formidable assistant for accelerating testing. But like any powerful tool, it comes with dangers. An AI can lie with confidence, reveal industrial secrets, or consume the energy of a small town.

In this chapter, we will learn how to identify, measure, and mitigate these risks to ensure impeccable software quality.

🗺️ Chapter roadmap

We will explore the shadow zones of generative AI through three major axes:

  1. Quality failures (section 3.1): how to detect hallucinations, reasoning errors, and cognitive biases in GUS.
  2. Security risks (section 3.2): how to prevent personal data leaks and "Prompt Injection" attacks.
  3. Societal and environmental impact (section 3.3): understanding LLM energy consumption and ensuring compliance with new laws (such as the AI Act).

As a quality professional, your role is evolving: you are no longer just hunting for functional bugs, you are becoming the guardian of AI ethics and reliability.


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