Security, Privacy, and Data Governance

Study the policies and technical controls needed to protect user data and reduce misuse in generative AI systems.

Explanation

Security includes access control, secrets management, safe tool execution, and auditability.

Privacy includes data minimization, retention rules, consent, and careful logging practices.

Governance defines who can access which data, how models are used, and how risks are reviewed.

Why this topic matters in practice

In generative AI products, the model is only one part of the system. The surrounding workflow determines whether the output is useful, safe, and maintainable. This lesson matters because it helps you connect the idea to tasks such as tutoring, search, copilots, business assistants, and production automation.

Examples

Masking sensitive values before logging

The code below is intentionally concise so the underlying pattern stays clear. It focuses on the application logic you can reuse, even if you later switch model providers or deployment environments.

def mask_email(email):
    name, domain = email.split("@")
    return name[0] + "***@" + domain

print(mask_email("student@example.com"))

How the coding section works

• Masking is one small part of privacy-aware system design.
• Security and privacy controls should be planned early, not added at the end.
• Generative AI apps often touch sensitive inputs, so governance matters.

Implementation advice

When turning this lesson into a real feature, think beyond the code snippet itself. Decide what inputs should be allowed, how you will validate outputs, how you will recover from errors, and how you will measure whether the feature is actually helping users. Those surrounding choices often determine whether an AI feature feels polished or unreliable.