What changes when we deploy OpenAI with sensitive data?
LLM Data Boundary Assessment
Using large language models with sensitive, regulated, or proprietary data introduces control requirements that differ significantly from general-purpose deployments.
Why This Question Matters
Default LLM configurations assume non-sensitive workloads. When sensitive data enters the picture—PII, PHI, financial records, or IP—the control surface expands dramatically. Teams that skip this assessment often discover compliance gaps after production deployment.
What the Output Will Cover
This output will map: data classification boundaries, prompt/response logging requirements, model access controls, data residency constraints, and audit trail requirements. You will receive a reference architecture showing where controls must exist, what assumptions apply, and what remains undecided.
Before You Begin
- • This assessment takes approximately 5 minutes
- • You will receive a shareable reference architecture
- • No vendor recommendations or product comparisons are included
- • All outputs state explicit assumptions and limitations