AI Risk Assessment Matrix - Public Overview

Overview

AI risk is more than "the model might be wrong." Technical accuracy accounts for only about 20% of AI risk. The other 80% spans operational challenges, legal exposure, and ethical concerns. Organizations that assess only technical risk are blindsided by the failures that actually occur.

This free overview introduces comprehensive AI risk assessment. It explains the 4-dimensional risk model, illustrates with real-world failures, and provides the foundation for building a complete risk management program.

What's Inside

• The Cost of Ignoring AI Risk: Real-world case studies—Amazon's biased hiring tool, Apple Card discrimination investigation, healthcare AI recalls, chatbot data breaches, model poisoning attacks
• The 4 Dimensions of AI Risk:
  • Technical: Model accuracy, security vulnerabilities, system failures, integration issues
  • Operational: Data quality, human oversight gaps, vendor dependencies, resource constraints
  • Legal/Compliance: Regulatory violations, IP issues, contractual obligations, liability gaps
  • Ethical/Reputational: Bias and discrimination, privacy violations, environmental impact, societal harm
• Why Failures Happen at Intersections: How multiple risk types combine to create failures
• Risk Scoring Fundamentals: Introduction to likelihood × impact methodology
• The AI Risk Landscape: What keeps CAIOs up at night—emerging threats and common vulnerabilities

Who This Is For

• Chief AI Officers building risk management programs
• Risk Managers extending ERM to AI systems
• Compliance Officers assessing AI regulatory risk
• Executives understanding AI risk exposure
• Anyone seeking an introduction to AI risk concepts

Why This Resource

Most AI risk discussions focus on technical accuracy or bias—missing the full picture. This overview provides comprehensive risk awareness, explaining all four dimensions and how they interact. Understanding the full risk landscape is essential before deploying assessments.

Real-world case studies make abstract risks concrete, showing how failures actually manifest.

FAQ

Q: What's the most common AI risk organizations face?

A: It depends on context, but operational risks (data quality, oversight gaps) and compliance risks (regulatory requirements) affect most organizations. The overview helps you identify which risks apply to your situation.

Q: How is AI risk different from traditional technology risk?

A: AI systems learn from data and make decisions in ways that create unique risks: bias from training data, model drift over time, lack of explainability, adversarial attacks. Traditional IT risk frameworks miss these AI-specific factors.

Q: Is this overview enough to assess our AI risks?

A: This overview provides conceptual foundation. For comprehensive risk assessment methodology, scoring frameworks, and mitigation playbooks, see our premium AI Risk Assessment Matrix.