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Generative AI Risk Map

An AI system is not secure because it "seems to answer well." It is secure when you know what can fail, how you measure it, who is accountable, and what controls reduce the harm.

  • Separate technical, legal, operational, and user risks.
  • Use a simple map inspired by NIST: govern, map, measure, and manage.
  • Decide when an AI app can be published and when it must remain in pilot.

Four questions before publishing

  • Govern: who decides, who reviews, and what is prohibited.
  • Map: which users, data, tools, and decisions the system touches.
  • Measure: which tests check for errors, bias, privacy, and abuse.
  • Manage: which limits, logs, permissions, and processes reduce risk.

Minimum risk card

Practical Responsible AI

Do not turn it into an abstract manifesto. For each system, review bias, accessibility, explainability, privacy, ability to appeal, and harm from automating too much.

Terminal
responsible_ai_check:
  affected_users:
    - customers
    - employees
  possible_biases:
    - language
    - technical_level
    - accent_or_audio
  mitigations:
    - human review for sensitive decisions
    - manual alternative available
    - logs without full personal data
    - brief explanation of limits to the user
  appeal: "human support email"
Terminal
riesgo:
  nombre: "responding with fabricated medical information"
  sistema: "support chatbot"
  usuarios_afectados: ["customers", "support team"]
  datos: ["questions", "ticket history"]
  probabilidad: medium
  impacto: high
  controles:
    - limit domain
    - respond with sources
    - abstain when there is no evidence
    - human review for sensitive cases
  tests:
    - questions without evidence
    - malicious instructions
    - personal data in prompt
  responsable: "product team"
  fecha_revision: "2026-07-03"
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.

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.

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.

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

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