IRHAI-POL-005 (RUNTIME EDITION)

Runtime Governability & Execution Control

(Deterministic Control of Probabilistic Systems)

Moving governance from static pre-deployment validation to active, execution-layer operational infrastructure.

⛨ DOCTRINAL BINDING STATEMENT

AI systems may generate probabilistic inference. Execution authority must remain deterministic and explicitly governed. The Runtime Governability Architecture below acts as an active admissibility layer, enforcing IRHAI-POL-005 by conditionally restricting execution based on real-time epistemic signals.

🏛

Governance Operational Definition

5.1 The Governability Imperative

Traditional AI governance models designed for static systems fail during runtime operation. Modern clinical AI operates continuously, adapting and influencing workflows probablistically. Deployment is not the end of governance; deployment is where governance begins. Governability demands systems remain monitorable, interruptible, and bounded.

The Failure of Static Governance:

✖
Pre-deployment validation ignores runtime semantic drift.
✖
Retrospective audits occur after irreversible clinical actions.
✖
Documentation provides zero deterministic execution control.

Runtime Governability Requirements:

✔
Admissibility: Dynamic gating of AI outputs.
✔
Containment: Deterministic authority boundaries.
✔
Degradation: Fail-safe state transitions (S2→S5).

> 5.3 PRINCIPLE OF ACCURACY VS SAFETY:

"Accuracy is statistical. Safety is structural."
// Inference layer generates probabilities.
// Governance layer enforces execution admissibility deterministically.

Execution Governance Pipeline

The mandatory separation of probabilistic inference from operational authority.

LAYER 1

CLINICAL DATA

→

↓

LAYER 2 (INFERENCE)

PROBABILISTIC ENGINE

→

↓

LAYER 3 (GOVERNANCE)

ADMISSIBILITY GATE

→

↓

LAYER 4 (EXECUTION)

DETERMINISTIC ACTION

Pipeline Idle. Press 'Simulate Pipeline' to visualize gating.

1. Clinical Vector Input

System ingests contextual patient data, workflow states, and external requests.

2. Probabilistic Generation

Model evaluates inputs and generates an inference. Crucially, this output possesses zero execution authority.

3. Admissibility & DAEE

The runtime governance plane intercept. Evaluates semantic drift and Decision Admissibility (DAEE) before granting authority.

4. Deterministic Execution

Only admissible outputs are executed, bound by institutional protocols and strictly gated containment bounds.

RGA Runtime Governance Console

⚙ DAEE: Admissibility Engine

Evaluates real-time "Decision Admissibility & Execution Eligibility". It monitors probabilistic outputs for epistemic uncertainty, hallucination markers, and semantic drift, scoring the system's current governability state.

🛡️ ACP: Authority Control Protocol

Enforces deterministic containment. Translates the DAEE state (S2-Normal, S3-Restricted, S4-Escalated, S5-Contained) into strict execution boundaries, ensuring AI systems never silently influence clinical workflows without proper gating.

Governability State

■ S2 (Normal)

Execution Authority

Advisory (Active)

Runtime Bound Status: ACP_ROUTING_SECURE

⚖

POLICY BINDING

[R1] Telemetry Active
[R2] Admissibility Gate
[R3] Authority Bounded
[R4] Orchestration Isolate
[R5] S5 Fail-Closed

Telemetry: Epistemic Confidence vs Semantic Drift

Real-time DAEE

DAEE Telemetry Object

{
  "inference_id": "inf-uuid",
  "semantic_drift": "float",
  "daee_status": "enum",
  "auth_route": "enum",
  "orchestration": "bool",
  "audit_hash": "sha256"
}

Live Intercept Payload

✔ GATE: PASS ✔ ACP BOUNDED

{
  "inference_id": "inf-88x2",
  "semantic_drift": 0.02,
  "daee_status": "admissible",
  "auth_route": "advisory",
  "orchestration": "secure",
  "audit_hash": "e3b0c442..."
}

Inject Runtime Constraints

Operational Trace Ledger

STATE HASH: 0000000000...

Governance Plane initialized. Telemetry active.

Institute For Responsible Healthcare AI

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IRHA - Policy 005: Governance as runtime operational control (Lifecycle Oversight)

EXECUTION LOCKED (S5)