Secure and private AI
A practical architecture for using models without treating sensitive business data as free training material.
Choose the boundary first
Some workloads can safely use a hosted model with contractual controls. Others require a private endpoint, a dedicated cloud environment, or a model hosted on infrastructure you control. The right answer follows the data classification and threat model.
Minimize what the model sees
Sensitive identifiers can be removed, tokenized, generalized, or replaced before a request leaves the trusted boundary. Retrieval should expose only the smallest relevant context and every permission check should happen outside the model.
Treat prompts and outputs as untrusted
Prompt injection, data exfiltration, hallucination, and unsafe tool calls are engineering problems. Constrained tools, allowlists, evaluation suites, human approval, and audit logs are more important than a clever system prompt.
Small projects can still matter
A focused data-masking gateway, secure document prototype, or model-hosting assessment can often be completed in a day. The result may be a production component, a tested proof of concept, or a clear go/no-go decision.