AIS101 · AI Risks: An Overview for Organizations

Categories: Cybersecurity
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About Course

Get a clear, practical map of the risks that come with using AI at work, from data leaks and wrong answers to bias, security threats and legal exposure, and learn how to start managing them.

Explore · 4 hours · 4 lessons, a lab, an assessed assignment and a portfolio piece

Who it is for

Managers, team leads, compliance staff and small-business owners who use or approve AI tools.

Prerequisites

No technical background is required; a general idea of what tools like chat assistants do is enough.

What you will be able to do

  • Describe how generative and predictive AI systems create value and where their failure modes come from
  • Classify AI risks into security, privacy, reliability, fairness, legal and operational categories
  • Build an inventory of AI tools in use, including unapproved shadow AI
  • Score and record AI risks in a simple risk register with owners and first controls

Lessons

  • Lesson 1 · How AI systems work and where they fail
  • Lesson 2 · A map of AI risks
  • Lesson 3 · Inventory, risk register and first controls
  • Lesson 4 · Lab and assignment

Lab: Build an AI use inventory and risk register

You create an inventory of AI tools used by a fictional 30-person marketing agency, including tools staff adopted on their own. For each use case you identify risks with the help of the MIT AI Risk Repository, rate likelihood and impact, and record an owner and a first control in a risk register.

Free tools: Google Sheets or LibreOffice Calc, the free MIT AI Risk Repository and the OWASP Top 10 for LLM Applications (both free online)

Assessed assignment

Write a two-page AI risk overview for the leadership of a small business you know or a fictional one. Describe at least five AI use cases, the main risks of each, a heat map of the top ten risks and three quick wins that can be implemented in the next 30 days. Write for non-technical readers and avoid vendor marketing claims.

Criterion Weight What excellent work looks like
Use case coverage 25% At least five realistic use cases, including shadow AI, are described with data involved and users.
Risk identification 30% Risks are specific, correctly categorized and linked to the use case rather than generic.
Scoring and prioritization 25% Likelihood and impact ratings are justified and the heat map reflects them consistently.
Quick wins and clarity 20% Quick wins are concrete, owned and achievable in 30 days; language is clear for leadership.

Portfolio piece

An AI use inventory, risk register and leadership risk overview for a small business.

Self-paced, 12-month access. Questions are answered in writing through the student panel. A completion record is issued when you finish the course.

Next step

Continue with AIS102 · Data Privacy Principles (LGPD, GDPR and Beyond) · 4 hours · $28.00

Continue to the next course

Prefer the whole program? Program in AI Security, Privacy & Governance · $277.00 (10% less than buying the courses separately)

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What Will You Learn?

  • Describe how generative and predictive AI systems create value and where their failure modes come from
  • Classify AI risks into security, privacy, reliability, fairness, legal and operational categories
  • Build an inventory of AI tools in use, including unapproved shadow AI
  • Score and record AI risks in a simple risk register with owners and first controls

Course Content

AIS101 · AI Risks: An Overview for Organizations (4 h)
Get a clear, practical map of the risks that come with using AI at work, from data leaks and wrong answers to bias, security threats and legal exposure, and learn how to start managing them.

  • Lesson 1 · How AI systems work and where they fail
  • Lesson 2 · A map of AI risks
  • Lesson 3 · Inventory, risk register and first controls
  • Lesson 4 · Lab and assignment
  • AIS101 Assessment · AI Risks: An Overview for Organizations
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