Large language models (LLMs) are extremely large, complex machine learning systems capable of routinely generating highly articulate, plausible-sounding—but not necessarily true—linguistic content in response to queries. This paper provides an in-depth, multifaceted analysis of LLMs to help data protection authorities effectively regulate and respond to the challenges of this new technology. The analysis is undertaken from three perspectives: (1) the technology itself, that is, how LLMs fundamentally work and are developed; (2) the privacy and data protection risks raised by them; and (3) the emerging set of best practices to reduce or eliminate these risks.

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