AI Risk Assessment Matrix - Tech & SaaS Edition

Overview

Platform-scale AI creates platform-scale risk. When your systems make billions of decisions daily, even rare failures affect millions of people. Content moderation errors expose users to harm. Recommendation algorithm biases shape public discourse. Account action mistakes wrongfully punish legitimate users.

This comprehensive risk assessment framework is built specifically for tech and SaaS companies managing AI at scale. It addresses the unique risk categories, amplification dynamics, and regulatory complexity that platform companies face—helping you identify, assess, and mitigate risks before they become crises.

What's Inside

• Tech AI Risk Categories: Comprehensive taxonomy covering user safety risks (content harms, harassment, radicalization), account/identity risks (wrongful actions, fake accounts, impersonation), platform integrity risks (manipulation, spam, coordinated campaigns), and recommendation risks (engagement optimization harms, filter bubbles, addiction)
• Content Moderation Risk Assessment: Framework for evaluating AI-powered content moderation including accuracy/recall tradeoffs, category-specific risk analysis, edge case handling, and appeal process design
• Recommendation System Risk Assessment: Evaluation methodology for recommendation algorithms including engagement vs. wellbeing tradeoffs, amplification analysis, diversity metrics, and filter bubble detection
• Privacy & Data Risk Assessment: Platform-specific privacy risks including data minimization, consent management, cross-product data use, and international data flows
• Risk Scoring Methodology: Quantitative framework for scoring risks by likelihood, impact, velocity (how fast harm spreads), and scale (number of affected users)
• Risk Mitigation Strategies: Platform-appropriate mitigations including circuit breakers, gradual rollouts, A/B testing for safety, human review escalation, and automated rollback triggers
• Risk Register Template: Structured format for documenting and tracking platform AI risks with ownership, status, and remediation tracking
• Monitoring at Scale: Metrics, dashboards, and alerting frameworks for detecting emerging risks across billions of interactions

Who This Is For

• Chief AI Officers at platform companies responsible for AI governance
• Trust & Safety Leaders managing content moderation and platform integrity
• Product Leaders building AI-powered features with user impact
• Risk Officers integrating platform AI into enterprise risk management
• Engineering Leaders implementing AI safety and monitoring at scale

Why This Resource

Generic risk frameworks don't address platform dynamics. This framework understands that platform AI risks compound and amplify—a single algorithm change can affect billions of users, viral dynamics can accelerate harm faster than human review can respond, and global operations mean navigating dozens of regulatory regimes simultaneously.

Every assessment methodology is designed for scale: batch evaluation processes, statistical sampling approaches, and automated monitoring that works across billions of interactions.

FAQ

Q: How is platform AI risk different from enterprise AI risk?

A: Scale fundamentally changes risk dynamics. Enterprise AI might affect thousands of decisions; platform AI affects billions. Viral dynamics mean harmful content or manipulation can spread globally in hours. Multi-jurisdictional operations create regulatory complexity. This framework addresses these platform-specific dynamics.

Q: Does this cover content moderation AI specifically?

A: Yes. Content moderation receives dedicated coverage including accuracy/recall tradeoffs (balancing over-removal vs. under-removal), category-specific risk analysis (violence, CSAM, misinformation each have different risk profiles), edge case handling, and appeal process design.

Q: How do we assess recommendation algorithm risks?

A: The framework includes specific methodology for recommendation systems: engagement vs. wellbeing tradeoffs, amplification analysis (what content gets disproportionate reach), diversity metrics (are users getting varied content or filter bubbles), and addiction/compulsive use indicators.