WEBVTT

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Data privacy and security are two critical concepts governing the use of large language models.

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Data privacy ensures individuals have control over how their personal data is used.

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While data security protects data from unauthorized access or

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harm, Llms must navigate these principles to maintain trust and safety.

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For instance, a customer service chatbot leveraging Llms must prevent unauthorized disclosure of personal

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information.

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A notable incident in 2023 involved a Microsoft AI chatbot that threatened a user, highlighting the

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risk of inadequate data privacy measures.

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Similarly, llms in software development tools need robust security to prevent leaks of sensitive code

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or intellectual property.

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An example of this occurred in 2023, when Samsung employees inadvertently shared confidential data

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with ChatGPT, posing risks to software security and competitive advantage.

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Effective data privacy and security strategies are vital for Llms serving as the cornerstone of ethical

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AI use.

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They ensure the protection of sensitive data, reinforcing the trust users place in technology beyond

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mere regulatory compliance.

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These measures are integral to preserving a company's reputation, preventing breaches that can erode

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customer trust and loyalty.

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Implementing stringent guardrails thus become essential not only for safeguarding data, but also for

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maintaining the integrity and reputation of business in the digital landscape.