AI Risk Identification and Measurement

This question asks whether you've actually figured out what could go wrong with your AI and how bad it could be. It's not enough to know that 'AI has risks' - you need to specifically identify risks like biased decisions, data breaches through AI, or AI making costly errors, and then measure how likely and severe each risk is. Think of it like a doctor not just saying 'you might get sick' but specifically identifying and rating your risk for different conditions.

Identifying and measuring AI risks is the foundation of responsible AI deployment and is increasingly required by enterprise customers and regulators. Without this, you're flying blind - unable to prioritize security investments, set appropriate insurance coverage, or make informed decisions about AI features. Companies that can demonstrate measured AI risks close deals faster, qualify for better insurance rates, and avoid costly surprises. Those that can't often face contract rejections, regulatory scrutiny, and are unprepared when AI incidents occur, leading to panic responses and poor decisions.

Many companies perform only superficial risk identification, listing obvious risks like 'AI might make mistakes' without diving into specific scenarios relevant to their use case. Another common error is measuring risks once during development but never updating the assessment as the AI system, data, and usage patterns evolve - risks change over time and your measurements need regular updates.