Which of the following risks most often occurs when developing an internal chatbot?

Data leakage is the most applicable risk because an internal chatbot commonly interacts with organizational information that may include confidential documents, customer information, employee records, credentials, intellectual property, and other sensitive content. CompTIA SecAI+ specifically identifies accidental data leakage as an AI risk and sensitive information disclosure as an attack or failure condition affecting AI systems. Internal chatbots can expose information when retrieval permissions are too broad, conversation context crosses security boundaries, prompts contain confidential material, or model responses reproduce information that the requesting user should not receive. Prompt injection is an important chatbot attack vector, but it describes deliberate manipulation of model instructions rather than the broader organizational risk emphasized here. Model theft concerns unauthorized acquisition or reproduction of a model. Unauthorized access concerns gaining access to resources without proper authorization and is generally addressed through model, data, agent, network, and API access controls. In contrast, information leakage can occur even when legitimate employees are using the chatbot normally. Protecting sensitive data through access controls, minimization, masking, classification, and monitoring is therefore fundamental to internal chatbot security.

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