A privacy-first LLM pipeline analyzing 500K items a day

New product features needed LLM-based analysis of customer data. Sending raw data to an LLM wasn't acceptable: it can contain PII, and customers need control over what is shared with a model.

• ▸About 500,000 items to analyze per day.
• ▸Part of replacing a third-party vendor tool with an in-house product.
• ▸Customer data that often contains PII, such as email addresses, across customers with different sensitivity requirements.

I architected and implemented the privacy-first pipeline, from sensitivity-based data handling through the LLM execution layer.

As a Security Champion, I already worked with the organization's data-classification standards, so I knew which fields were sensitive and how each had to be handled. The design started from those classifications instead of adding redaction afterwards.

• ▸Pipelines that handle each field according to its data-sensitivity level.
• ▸Customer-controlled settings: customers choose which data can be sent to the LLM and which can't.
• ▸Reversible pseudonymization: values like email addresses are replaced with placeholders before the request, the mapping stays in memory, and the real values are restored when the response comes back.
• ▸LLM routing across tiered models to balance result quality against API cost.
• ▸Server-Sent Events for real-time token streaming, and semantic caching to avoid redundant LLM calls.
• ▸Observability dashboards for guardrails, token usage, and model telemetry.

Privacy-ready LLM analysis at around 500K items a day. The model sees only what customers allow, PII is pseudonymized before it leaves, and responses are mapped back to real values, so result quality stays high.

• ▸Start privacy design from data classification, not from the model. Knowing which fields were sensitive, and how sensitive, shaped the whole pipeline.
• ▸Pseudonymize instead of deleting. Reversible placeholders keep the structure the model needs, so protecting PII didn't cost result quality.
• ▸Customer control builds trust. Letting customers decide what reaches the LLM made the feature easier to adopt.
• ▸Treat LLM calls like any other expensive dependency: route by need, cache repeated work, and watch tokens and cost from day one.