An AI risk assessment is a structured look at what could go wrong when your organization uses a particular AI tool. It covers how likely and how serious each problem would be, and what you will do about it. It usually records the use case, the data involved, the people affected, the risks, the controls you will put in place, and who signs off. For example, say your staff want to paste donor notes into a chatbot to draft thank-you letters. An assessment would ask whose information goes in, what happens if it leaks or the letters contain mistakes, and who approved the plan.
Why it matters
A risk assessment turns a vague worry into a short written record you can check later. It is not a one-time job. Repeat it when the tool changes, when you start feeding it different data, or when you use it for something new. When a vendor pitches an AI feature, ask what data it will see, where that data goes, and how you can turn it off. Put the answers in your assessment. The AI risk assessment template on this site gives you a place to start. Assessments fit inside wider AI governance, and frameworks such as the NIST AI RMF go deeper.
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