Implementing LLMs: Security and Privacy Considerations

Implementing LLMs: Security and Privacy Considerations

Deploying large language models inside the enterprise is no longer a research project — it is a production discipline. The companies getting it right share four traits.

Data boundaries

Private data never leaves the trust boundary. Use VPC-hosted inference, customer-managed keys, and retrieval architectures that pull from approved sources only.

Identity-aware prompts

Every prompt is bound to the calling user. Authorization is checked before retrieval, not after generation.

Output filtering

Responses pass through a classifier that strips PII and rejects unsafe completions.

Continuous evaluation

A red-team suite runs on every model and prompt change. No silent regressions.