Where Production LLM Pipelines Leak User Data: PII, Residency, and the Compliance Patterns That Hold Up
Most teams building LLM applications treat privacy as a model problem. They worry about what the model knows — its training data, its memorization — while leaving gaping holes in the pipeline around it. The embarrassing truth is that the vast majority of data leaks in production LLM systems don't come from the model at all. They come from the RAG chunks you index without redacting, the prompt logs you write to disk verbatim, the system prompts that contain database credentials, and the retrieval step that a poisoned document can hijack to exfiltrate everything in your knowledge base.
Gartner estimates that 30% of generative AI projects were abandoned by end of 2025 due to inadequate risk controls. Most of those failures weren't the model hallucinating — they were privacy and compliance failures in systems engineers thought were under control.
