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Lesson

GDPR basics for using AI without surprises

You don't need to be a lawyer to work smarter: you need to know what data you put into AI, what for, where it goes, and who can review it. This lesson doesn't replace legal advice, but it gives you a prudent framework.

  • Reduce personal data before using AI.
  • Distinguish local AI, cloud providers, and data processors.
  • Create a minimal checklist before automating client tasks.

Minimal checklist

  • Purpose: what the AI is used for.
  • Data: which fields go in and which are removed.
  • Provider: local, self-hosted, or external service.
  • Access: who can see input, output, and logs.
  • Retention: how long you keep results.
  • Human review: which actions require approval.
Terminal
Before sending data to a model:
1. Remove unnecessary fields.
2. Replace names with internal IDs when possible.
3. Do not include sensitive data unless clearly necessary.
4. Do not allow automatic sending without review.
5. Record what the system did and who approved it.
Ollama for an SME: a local, secure, and measurable trialFrom pilot to reliable flow: schema, sources, abstention and validation
Complete Aulafy mapSee how this lesson fits without leaving your path.

Complete Aulafy map

How all courses connect

This is not a checklist. Start with the foundation, choose an outcome, and go deeper only when your project needs more control.

  1. 1Understand
  2. 2Apply or build
  3. 3Operate with confidence
01

Choose an application

Turn the foundation into a visible outcome: a website, a business improvement, media, or an interactive experience.

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Continue into the technical branch when you need to maintain code, data, or infrastructure.

02

Build with code

Prepare your environment, work with coding agents, and run models while keeping control of your projects.

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This branch prepares you to design and operate reliable AI systems.

03

Take systems to production

Combine retrieval, agents, evaluation, security, deployment, and model adaptation when the problem requires it.

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You do not need every course: choose the component your system needs and return as it grows.

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