Deploying Generative AI (GenAI) in production comes with a range of risks that can impact ethical, operational, and legal dimensions. One of the most pressing concerns is hallucination, where models generate inaccurate or entirely false information, potentially misleading users. This becomes particularly critical in domains like healthcare, finance, or legal documentation. Another key risk is data privacy and leakage. If the model has been trained on sensitive or proprietary data, there's a chance it may unintentionally output confidential information.

There’s also the issue of bias. Generative models may inherit and amplify societal or dataset-specific biases, leading to discriminatory outcomes. Intellectual property infringement is another challenge, especially when models reproduce copyrighted content. Moreover, model misuse such as generating deepfakes or misinformation raises security and ethical concerns.

On the technical side, high compute and energy consumption, lack of interpretability, and challenges with model versioning and monitoring make long-term deployment complex. Therefore, deploying GenAI systems requires robust governance, ethical frameworks, and continuous validation pipelines to ensure accountability and reliability.

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