From the course: Introduction to NLP and LLMs: Principles and Practical Applications
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Privacy and data security
From the course: Introduction to NLP and LLMs: Principles and Practical Applications
Privacy and data security
- [Instructor] Let's look at a real-world use case to examine privacy and security challenges in NLP LLM deployment, focusing on practical measures for data protection and compliance. Data security and privacy are a critical concern for NLP LLM systems, especially when handling sensitive user information. Without proper safeguards, the risk of breaches and unauthorized access increases. Privacy and data security challenges include handling sensitive data because user queries often contain personal identifiers or sensitive information, or risks, which is unauthorized access to data, exposing private information. For example, a chatbot system storing unencrypted user data creates a number of vulnerabilities and violations. Privacy is about ensuring individuals have control over their personal data and how it is used. It is essential for compliance and user trust. The data lifecycle. Collection, storage, and use is critically important in the context of NLP LLMs and is directly governed…
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