Deterministic control for PHI-sensitive AI workflows.
BeaconGuard deploys alongside existing healthcare applications as an inline policy proxy. It enforces approved-pathway, role, trust, and policy checks before AI model execution, preserving reviewable evidence for privacy, security, compliance, risk, and architecture teams.
Existing Healthcare Systems Remain in Place
BeaconGuard does not alter or replace the EHR, clinical decision workflows, case-management tools, compliance programs, SIEM, IAM, or existing healthcare operational applications. It acts as a non-disruptive policy boundary that interposes at the AI request layer, enforcing role-based controls, deterministic context tags, and approved-pathway verification before patient-related context is permitted to reach an approved model endpoint.
AI-assisted PHI-sensitive healthcare operations review.
Healthcare teams are evaluating AI for operational summaries, staff-assist workflows, care-management support, discharge planning, patient communication drafting, and utilization review preparation. These workflows may involve patient-related context, restricted operational information, or PHI-sensitive inputs. BeaconGuard provides an inline policy boundary before model execution so the organization can enforce approved pathways and preserve evidence.
Healthcare Control Points
- Approved model pathway verification
- Deterministic context tag evaluation
- User role and workflow authorization
- Source-system trust validation
- Signed request metadata validation
- Freshness and replay controls
- Needs-review routing
- Audit-ready evidence record generation
Healthcare Architecture Disclaimer
BeaconGuard does not rely on probabilistic PHI discovery as the authorization boundary. Host applications or optional preprocessors may generate deterministic context metadata tags. BeaconGuard evaluates those tags, signed request attributes, workflow identity, user role, approved-pathway status, and policy inputs before model execution.
BeaconGuard evaluates deterministic context tags, source-system metadata, signed request attributes, workflow identity, user role, approved-pathway status, and policy inputs before model execution.
Healthcare Scenarios
Unapproved External Model for PHI-Heavy Summary
Condition: A care-management workflow requests an AI-generated summary of discharge-planning notes, recent encounters, and social-work comments. The request is pointed to an unapproved external model endpoint.
BeaconGuard action: BeaconGuard denies the request or routes it for privacy review because the workflow lacks an approved PHI-handling pathway.
Why it matters: Staff may believe the request is operationally useful, but sending patient-related context to an unapproved endpoint creates privacy, contractual, and audit exposure.
Reviewer outcome: The reviewer can see that model execution was blocked until approved-pathway and policy conditions were satisfied.
Role or Minimum-Necessary Violation
Condition: A contractor or non-clinical support user requests an AI-generated utilization-review summary that includes restricted patient-related context.
BeaconGuard action: BeaconGuard denies the request because user role, workflow scope, context tags, or minimum-necessary policy conditions are not satisfied.
Why it matters: Access to patient-related context should not expand because an AI tool can summarize it.
Duplicate Patient Outreach / Replay Condition
Condition: A patient-message drafting workflow resubmits the same request because a front-end process retries with stale request identifiers.
BeaconGuard action: BeaconGuard denies the duplicate request using request ID, timestamp, freshness window, signed metadata, or deployment-specific replay controls.
Why it matters: Duplicate inference can create conflicting drafts, duplicate patient outreach, or ambiguous review evidence.
Source Trust Failure on Clinical or Referral Content
Condition: A referral-intake or chart-abstraction workflow submits documents whose origin, signature, or source-system trust markers cannot be validated.
BeaconGuard action: BeaconGuard denies the request before model execution because provenance and trust prerequisites were not established.
Why it matters: If source content cannot be trusted, the organization should not rely on a model to process or summarize it inside a regulated operational workflow.
What BeaconGuard Does Not Replace
- BeaconGuard does not replace the EHR.
- BeaconGuard does not replace clinical workflows.
- BeaconGuard does not replace clinical safety review.
- BeaconGuard does not provide diagnosis or treatment recommendations.
- BeaconGuard does not replace a HIPAA compliance program.
- BeaconGuard does not replace legal review.
- BeaconGuard does not replace IAM, SIEM, GRC, SOC 2, HITRUST, or security program controls.
- BeaconGuard is the runtime policy and evidence boundary at the AI request layer.