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Local-First Privacy & Trust

Clinical Corvus enforces a strict 'Local-First' Trust Boundary. By default, all reasoning and ingestion occur within the secure backend, minimizing the exposure of Patient Health Information (PHI).

caution

Privacy and security are fundamental to Clinical Corvus. This document describes our privacy approach. Always follow your institution's data handling policies and applicable regulations (HIPAA, LGPD, etc.).

Egress-Filtered Information Retrieval​

When external evidence is needed, Clinical Corvus uses an egress-filtered approach: external calls should carry clinical keywords, not patient-identifying context.

When the Clinical Research Agent (CRA) requires external evidence (e.g., searching PubMed or the Open Web), it employs a Local Sanitization Layer:

  1. Sanitization: A rule-based/regex layer strips PHI (names, MRNs, dates) in-process before any network request is formed.
  2. Anonymized Queries: External search providers receive only anonymized clinical keywords (e.g., "septic shock protocols", "vancomycin dosing"), without patient context.
  3. Prevention: The architecture is designed to prevent 'leakage by default' rather than relying solely on post-hoc filtering.

If an institution prefers to use its own model endpoints (e.g., a private OpenAI/Azure/other account), Clinical Corvus can be pointed to those credentials so data stays within that governance boundary.

Ephemeral Memory Architecture​

To align with data sovereignty requirements (GDPR/LGPD), the system avoids long-term retention of raw patient data on Corvus servers.

  • CaseState: Persists structured, episode-scoped reasoning state (snapshots + patches) on institutional infrastructure (local storage).
  • AgentMemoryService: Stores short-term event timelines (Redis) for conversation continuity.
  • Data Lifecycle Management: Automated TTL (Time-To-Live) sweeps sanitize ephemeral logs and age-out long-term entries, ensuring compliance without manual intervention.

Agentic Safety Layers​

Trust is not just about privacy; it's about the reliability of the clinical advice. Corvus implements active safety patterns:

1. Goal Verification​

Before returning any response, a dedicated verification loop (VerifyGoalCompletion) assesses if the drafted answer actually addresses the user's core intent.

2. The Critic Agent​

A "Producer-Critic" pattern employs a separate adversarial model to critique responses for accuracy, completeness, and clarity. It flags responses with INFO, WARNING, or ERROR severity before they reach the clinician.

3. Confidence-Based Escalation​

A formalized trust model (Low, General, High Clinical, Critical Life Safety) automatically triggers a "Human-in-the-Loop" pause (HitlPauseTool) if confidence dips below the threshold required for the specific care setting (e.g., < 0.98 for Critical Safety).

Security Posture​

  • Content-Security-Policy (CSP): Strict headers to prevent XSS.
  • Audit Logging: Deterministic logging for all PHI-touching endpoints.
  • Rate Limiting: Centralized rate limiting to prevent abuse.
  • Opt-In Compliance: For deployments using hosted models (e.g., GPT-4), third-party endpoints are reachable only if explicit Zero-Data Retention (ZDR) and BAA/DPA agreements are verified.