Sort AI risk signals

Sort each AI warning signal into the control it most directly calls for. Narrow the scope Test and measure Human oversight Data protection The assistant is wrong mainly on billing and contract questions Overall accuracy is high but one customer segment has more false declines The output could remove a benefit or block an application Training examples include credentials and private customer notes The use case started as summarization but now suggests final decisions A vendor model update may change behavior without notice Reviewers cannot override a model recommendation easily Source documents include opt-out customer records

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