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🟢 Foundation Path - AI GRC Fundamentals

📋 Overview

Duration: Weeks 1-4 Level: Beginner Focus: AI fundamentals, ethics, and governance principles

This foundation path establishes the core knowledge needed for AI governance, risk, and compliance work. You'll build understanding of AI concepts, ethical principles, and the governance landscape.

🎯 Learning Objectives

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

  • ✅ Explain AI and machine learning concepts to non-technical stakeholders
  • ✅ Describe major AI governance frameworks and their purposes
  • ✅ Articulate ethical principles underlying responsible AI
  • ✅ Identify AI use cases and their associated risks
  • ✅ Understand the regulatory landscape for AI

📅 Week 1: AI Fundamentals

Learning Objectives

  • Understand what AI and machine learning are
  • Distinguish between AI types (narrow, general, supervised, unsupervised)
  • Recognize AI applications in business contexts

Required Resources

Resource Type Time Cost Link
AI For Everyone (Andrew Ng) Course 4-6 hrs Free Coursera
Elements of AI (Part 1) Course 3-4 hrs Free elementsofai.com

Supplementary Resources

Resource Type Time Link
StatQuest: ML Fundamentals Videos 2 hrs YouTube
Google AI Essentials Course 2 hrs Google

Hands-On Exercises

  1. AI Use Case Identification

    • List 5 AI applications you interact with daily
    • Categorize each as: recommendation, classification, generation, or prediction
    • Document potential risks for each

  2. Stakeholder Explanation

    • Write a 1-paragraph explanation of machine learning for a board member
    • Focus on business impact, not technical details

Week 1 Checklist

  • Complete AI For Everyone course
  • Start Elements of AI
  • Complete AI use case exercise
  • Write stakeholder explanation

📅 Week 2: AI Ethics & Principles

Learning Objectives

  • Understand foundational AI ethics principles
  • Recognize bias and fairness issues in AI
  • Articulate the importance of transparency and accountability

Required Resources

Resource Type Time Cost Link
OECD AI Principles Document 1 hr Free OECD AI
Ethics of AI (Elements of AI Part 2) Course 3-4 hrs Free Building AI
AI Ethics Brief (IEEE) Document 1 hr Free IEEE

Supplementary Resources

Resource Type Time Link
Fairness and Machine Learning (Book) Reading 4 hrs fairmlbook.org
AI Incident Database Exploration 1 hr incidentdatabase.ai

Hands-On Exercises

  1. OECD Principles Mapping

    • Read the 5 OECD AI Principles
    • For each principle, identify one organizational practice that supports it
    • Document gaps in your current environment

  2. AI Incident Analysis

    • Select 3 incidents from the AI Incident Database
    • For each: identify the ethical principle violated and potential preventive controls

Week 2 Checklist

  • Read and summarize OECD AI Principles
  • Complete Elements of AI Part 2
  • Review IEEE AI Ethics guidelines
  • Complete incident analysis exercise

📅 Week 3: Introduction to AI Governance

Learning Objectives

  • Understand the governance landscape for AI
  • Identify key regulatory frameworks
  • Recognize the role of standards in AI governance

Required Resources

Resource Type Time Cost Link
NIST AI RMF Overview Document 2 hrs Free NIST AI RMF
AI Governance Primer (WEF) Report 1 hr Free WEF
ISO 42001 Overview Document 1 hr Free ISO

Supplementary Resources

Resource Type Time Link
EU AI Act Summary Article 30 min artificialintelligenceact.eu
AI.gov Resources Portal 1 hr ai.gov

Hands-On Exercises

  1. Framework Comparison

    • Create a table comparing NIST AI RMF, ISO 42001, and EU AI Act
    • Identify: purpose, scope, mandatory/voluntary, key requirements

  2. Governance Gap Assessment

    • Using the NIST AI RMF GOVERN function
    • Assess your organization against 3-5 key practices
    • Document current state and gaps

Week 3 Checklist

  • Read NIST AI RMF executive summary
  • Review ISO 42001 scope and structure
  • Read WEF AI Governance Primer
  • Complete framework comparison exercise

📅 Week 4: AI Risk Fundamentals

Learning Objectives

  • Understand AI-specific risks
  • Apply risk assessment concepts to AI
  • Recognize the AI risk lifecycle

Required Resources

Resource Type Time Cost Link
MIT AI Risk Repository Database 2 hrs Free airisk.mit.edu
NIST AI RMF Playbook Guide 2 hrs Free NIST Playbook
AI Risk Landscape (Berryville) Paper 1 hr Free BIML

Supplementary Resources

Resource Type Time Link
ENISA AI Threat Landscape Report 2 hrs ENISA
McKinsey AI Risk Article 30 min McKinsey

Hands-On Exercises

  1. Risk Category Mapping

    • Using MIT AI Risk Repository, identify top 10 relevant risks
    • Map each to: likelihood (H/M/L), impact (H/M/L), current controls

  2. Risk Register Creation

    • Create a basic AI risk register template
    • Populate with 5 risks from your environment
    • Include: risk description, category, owner, controls, status

  3. Foundation Assessment

    • Self-assess your learning across all 4 weeks
    • Identify areas needing reinforcement
    • Plan targeted review

Week 4 Checklist

  • Explore MIT AI Risk Repository
  • Work through NIST AI RMF Playbook sections
  • Read Berryville AI Risk Landscape
  • Complete risk register exercise
  • Conduct foundation self-assessment

📊 Foundation Path Assessment

Knowledge Check

Test your understanding with these questions:

  1. What are the key differences between supervised and unsupervised learning?
  2. Name the 5 OECD AI Principles
  3. What are the 4 functions of the NIST AI RMF?
  4. What is the scope of ISO 42001?
  5. Identify 3 categories of AI risk

Portfolio Deliverables

By completing this path, you should have:

  • AI use case inventory
  • Stakeholder explanation document
  • OECD principles mapping
  • AI incident analysis
  • Framework comparison table
  • Governance gap assessment
  • AI risk register

➡️ Next Steps

Congratulations on completing the Foundation Path!

Continue to: Regulatory Path - Deep dive into specific frameworks and regulations

📚 Additional Resources

Books

  • "Artificial Intelligence: A Modern Approach" - Russell & Norvig
  • "Weapons of Math Destruction" - Cathy O'Neil
  • "The Alignment Problem" - Brian Christian

Podcasts

  • AI Today Podcast
  • The AI Podcast (NVIDIA)
  • Practical AI

Communities

Last Updated: 2024 | Back to Learning Paths