A company's development team plans to use a public, third-party AI assistant to help write and optimize proprietary software. A Linux administrator raises a concern related to data governance, noting that the AI service's terms state that user prompts may be used for LLM training. What is the primary data security risk in this scenario?
Proprietary algorithms and sensitive data from the code could be absorbed by the LLM and subsequently exposed to other users.
The AI service will consume excessive network bandwidth, impacting other critical development services.
The LLM will generate inefficient code that does not adhere to the company's internal coding standards.
Using the AI assistant will require granting it direct, agent-based access to the company's private Git repositories.
The correct answer is that proprietary algorithms and sensitive data could be absorbed by the LLM and exposed. When a public LLM uses user prompts for training, any proprietary code, trade secrets, or sensitive data included in those prompts can become part of the model's knowledge base. This creates a significant risk that the model might later reproduce this confidential information in response to queries from other users, leading to intellectual property theft and data leakage. The other options are incorrect. Inefficient code generation is a performance or quality issue, not a data security risk related to training data. Granting direct repository access is a separate security concern not described in the prompt-based scenario. Excessive network bandwidth is an operational issue, not a data security risk.
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