The strongest guarantee you can make
Every privacy control in this course — detection, redaction, pseudonymisation, the gateway pattern — exists to mitigate the risk of data leaving your environment. Local inference eliminates that risk entirely. If the data never leaves, there is no cross-border transfer, no third-party processing, no retention by a provider, no training data contamination, and no metadata exposure. The privacy guarantee is architectural, not contractual.
This is not theoretical. As of early 2026, local models have reached a capability threshold where they can handle a meaningful share of enterprise AI workloads. Gemma 4 E4B running on a single NVIDIA L4 GPU can perform text classification, entity extraction, summarisation, Q&A over documents, and structured data extraction at quality levels that would have required cloud models two years ago. Larger local deployments — Llama 3.3 70B on a multi-GPU server, or Mistral models on an on-premises cluster — can handle more complex reasoning tasks.
The question is no longer "can we run AI locally?" It is "which workloads should run locally, which should go through the gateway, and which genuinely need cloud?"
