Understanding the full spectrum of AI risks enables comprehensive risk management programs.
## Risk Categories
### Technical Risks - Model Performance: Accuracy, reliability, and failure modes - Data Quality: Biased, incomplete, or poisoned training data - Distribution Shift: Model degradation as real-world data changes - Adversarial Attacks: Deliberate attempts to fool AI systems - System Integration: Failures at AI-system boundaries
### Operational Risks - Dependency Risk: Over-reliance on AI for critical decisions - Talent Risk: Shortage of AI expertise for maintenance - Vendor Risk: Third-party AI providers changing or discontinuing services - Scalability Risk: AI systems failing under load - Change Management: Organizational resistance to AI adoption
### Compliance and Legal Risks - Regulatory Non-compliance: Violations of AI regulations - Intellectual Property: Copyright issues with AI-generated content - Liability: Who is responsible for AI-caused harm - Privacy Violations: Unauthorized processing of personal data - Discrimination: AI systems with disparate impact
### Reputational Risks - Public Perception: Negative media coverage of AI failures - Bias Incidents: Publicized AI discrimination cases - Misinformation: AI-generated false content spread - Trust Erosion: Loss of customer confidence in AI systems
## Risk Scoring Framework ``` Risk Score = Likelihood × Impact × Velocity
Likelihood Scale (1-5): 1: Rare (<5% probability) 2: Unlikely (5-20%) 3: Possible (20-50%) 4: Likely (50-80%) 5: Almost Certain (>80%)
Impact Scale (1-5): 1: Negligible (no meaningful harm) 2: Minor (limited impact) 3: Moderate (significant disruption) 4: Major (severe harm) 5: Critical (existential threat)
Velocity Scale (1-3): 1: Slow (days to weeks to materialize) 2: Medium (hours to days) 3: Fast (immediate) ```