Observations on LLMs for Privacy Work

Drawing from hands-on experience, privacy engineer Damien Desfontaines assesses the pragmatic utility of large language models across privacy workflows. While LLMs accelerate policy interpretation, compliance checklist reviews, and synthetic documentation drafting, they remain unreliable for strict anonymization guarantees and formal differential privacy proofs. The piece provides a grounded counterpoint to automated compliance hype, highlighting that LLMs function best as assistive drafting tools rather than autonomous privacy arbiters.