Security

DT4H security focuses on protected modeling, runtime integrity, and auditability.

Security posture for cohort intelligence, Twin initialization, calibration pipelines, longitudinal runtime systems, and SETPOINT integration boundaries.

Security diagram #

INPUTSignals

Protected ingestion · normalized evidence · provenance

MODELDT4H Runtime

Cohorts · Twin state · calibration events · audit trail

COMPUTEStateK

State · confidence · transition · access boundaries

GOVERNControls

Least privilege · auditability · runtime observability

Security responsibilities #

01

Least privilege

Access to modeling, runtime, and calibration systems should be scoped by role.

02

Auditability

Calibration, state transitions, and runtime events should remain traceable.

03

Data integrity

Longitudinal state and cohort assignments require integrity protection.

04

Runtime observability

System health, signal freshness, and calibration state should remain visible.

05

Layer separation

Modeling, state computation, and execution should remain architecturally separated.

06

Governance controls

Clinical and research workflows require explicit boundaries and review.

Protected runtime surfaces #

Signal ingestionProtect raw and normalized health-related inputs from tampering.
Cohort resolutionAudit population-context assignment and reference-prior selection.
Twin stateProtect longitudinal state, confidence, and trajectory memory.
Calibration eventsTrace evidence updates, drift detection, and model refinements.
Execution feedbackCapture SETPOINT outcomes without collapsing execution into diagnosis.

Security boundaries #

IdentityRole scoped

Different users should see different layers of modeling and runtime data.

RuntimeObservable

Model health, signal freshness, and calibration state should be inspectable.

EventsAuditable

State transitions and recalibration events should remain traceable.

ClinicalGoverned

Clinical deployment requires validation, oversight, and explicit boundaries.

Implementation notes #

Protect model-changing events

Calibration, Twin updates, and state transitions should be auditable because they change downstream behavior.

Separate user-facing and operator-facing access

Consumers, clinicians, researchers, and operators should not share the same visibility layer.

Log runtime provenance

Every high-impact runtime output should be traceable to signal freshness, confidence, and evidence source.

LayerGovernance Framework
StatusActive Draft
SystemDT4H / StateK / SETPOINT
BoundaryInfrastructure, not diagnosis
System lineageDT4HTwinStateKSETPOINTOutcomesRecalibration
Infrastructure boundaryDT4H models cohorts, Twins, calibration, and runtime state. It does not diagnose, prescribe, or replace licensed clinical judgment.
Document statusInfrastructure draft
Last updatedMay 2026
Applies toDT4H.ai / AvatarK.ai ecosystem