A privacy and governance layer for clinical AI workflows, designed to reduce model-facing PHI exposure while keeping answers useful and policy-aware.
Why clinical AI needs enforceable governance beyond a polished safety story.
Healthcare organizations want to leverage generative AI platforms like OpenAI, AWS Bedrock, and Google Gemini to analyze complex clinical notes, extract insights, and assist doctors.
But PHI exposure risk makes ordinary AI workflows difficult to approve, govern, and trust in real clinical settings.
Warden 1.0 creates an AI safety boundary, helping demonstrate how clinical intelligence can be paired with tokenization, guardrails, and request checks.
Warden 1.0 adds request guardrails and policy checks around the clinical AI workflow.
How Warden 1.0 adds privacy boundaries and request checks to the workflow.
Patient-identifying information is prohibited from model exposure while the workflow preserves enough clinical context for useful reasoning.
AI requests are screened against safety policies to reduce prompt injection risk, block unauthorized data access, and keep clinical workflows within approved boundaries.
AI requests are kept inside defined workflow limits, reducing sensitive-data exposure while preserving useful clinical context.
Security controls aligned with NIST and HIPAA priorities.
Map, Measure, Manage: Warden 1.0 helps teams identify AI risks, apply request-level guardrails, and support governance reviews with evidence.
Access Controls: Supports least-privilege design by keeping sensitive data exposure limited across AI-assisted workflows.
Technical Safeguards: Designed around HIPAA Security Rule safeguards (§164.312), including controls that prohibit PHI from being read by the LLM.