Run AI on protected health data inside the hospital — and collaborate across institutions without pooling records or exposing PHI.
Protected health information cannot leave the clinical environment. HIPAA requires data-in-use protection, audit trails, and strict access controls that cloud AI APIs cannot provide without BAAs, shared infrastructure, and legal risk.
Private AI for healthcare: inference, RAG on clinical knowledge, guardrails, and HIPAA-aligned governance — running inside the hospital, never sending PHI anywhere.
Cross-institution AI research without pooling patient records — each institution's data stays sealed in a TEE.
Explore Prism AIHIPAA's Technical Safeguard standards require access controls, audit controls, integrity controls, and transmission security for systems handling Protected Health Information. AI inference on clinical data must occur in a HIPAA-compliant environment with documented access controls, a complete audit trail of every interaction, and technical measures preventing unauthorized disclosure. On-premise Cube AI deployment satisfies these requirements — PHI is processed inside the hospital, never transmitted to a third-party AI vendor.
A HIPAA BAA establishes contractual obligations with the cloud AI vendor but does not change the underlying technical architecture: PHI still leaves your environment on every API call, processed on infrastructure you do not control. The BAA transfers legal liability but does not eliminate the technical exposure. On-premise AI deployment eliminates the exposure entirely — PHI never leaves the clinical environment.
HIPAA-compliant AI deployment means running AI inference on Protected Health Information inside your own clinical infrastructure — with access controls limiting who can submit prompts, an audit trail of every interaction, PII/PHI detection and redaction guardrails, and data that never traverses a network path to a third party. Cube AI provides all of these as built-in infrastructure controls, not application-layer patches.
Prism AI enables multi-institution AI collaboration using Trusted Execution Environments. Each hospital's patient data stays sealed in its own TEE — other institutions and the TEE operator cannot read it. A shared research model is trained or evaluated across all institutions' data with only the aggregate result returned. This satisfies both HIPAA's minimum necessary standard and IRB requirements for multi-site research data handling.
PHI redaction automatically detects and removes Protected Health Information from AI inputs and outputs before they are processed or stored. Cube AI integrates Microsoft Presidio for PII and PHI detection — covering names, dates, geographic identifiers, phone numbers, email addresses, SSNs, medical record numbers, and other HIPAA-defined identifiers. Presidio runs inside your perimeter as part of the guardrails pipeline; detected PHI is redacted before the prompt reaches the model.
Yes. The EU AI Act classifies AI systems used for medical purposes — including clinical decision support, patient data analysis, and diagnostic assistance — as high-risk AI systems. High-risk AI requirements are fully enforceable from August 2026. Requirements include risk management systems (Article 9), data governance (Article 10), technical documentation (Article 11), and automatic logging of operation (Article 12). On-premise AI with Cube AI's audit trail and governance satisfies the architectural requirements for all four articles.
Talk to the team about HIPAA-aligned AI deployments, clinical data governance, and cross-institution collaboration.
