IDNA Reference Model — Execution Frontiers for Governed Autonomy

Canonical Specification | Registry Reference: NF-RA-001
Author: NovaFuse Technologies
Date: July 2026
Version: 1.0.0 (Draft v0.1)
Classification: Public Reference Model / Voluntary Standard
Companion Publications:
  CS-WP-001 - Cyber-Safety: The Paradigm Shift
  CS-TR-001 - Operationalized Cyber-Safety: Runtime Admissibility Architecture
  ERI-LX-001 - Executable Proof Lexicon

Abstract

This document introduces IDNA, a reference model that identifies four coupled runtime execution frontiers—Identity, Data, Networks, and AI—that must be governed together to support the cyber-safe operation of systems with autonomous or semi-autonomous computational components. IDNA is technology- and sector-agnostic, and is designed to complement existing risk management frameworks, including the NIST AI Risk Management Framework (AI RMF).

1. Purpose and Scope

1.1 Purpose

IDNA provides a compact, interoperable vocabulary and structure for describing where safety and trust must be established at runtime when autonomous computation may produce real-world effects.

1.2 Scope

IDNA applies to: * AI systems embedded in decision workflows (e.g., clinical decision support). * Tool-using or agentic systems (systems that can act via APIs or tools). * Safety-critical systems integrating probabilistic components.

IDNA does not prescribe specific technical controls (e.g., OAuth, SIEM, specific anomaly detectors). Instead, it defines outcomes and frontiers that implementations can satisfy using context-appropriate controls—consistent with NIST’s outcome-oriented approach.

2. Audience

IDNA is intended for: * System owners and risk officers establishing runtime assurance. * Security and platform architects implementing enforcement points. * Auditors and assessors evaluating evidence of controlled execution. * AI engineers integrating models into governed systems.

3. Document Conventions (Normative)

Key words "SHALL", "SHOULD", and "MAY" in this document are to be interpreted as follows: * SHALL indicates a mandatory requirement. * SHOULD indicates a recommended requirement. * MAY indicates an optional capability.

4. IDNA Concept Model

4.1 Core Premise: Cyber-Safety as Runtime Admissibility

IDNA operationalizes a system safety stance that separates: 1. Hypothesis generation (probabilistic model outputs), from 2. Admissibility and authority (deterministic enforcement).

The model generates hypotheses, while the Observer is the authority that admits/refuses outputs and writes the system-of-record.

4.2 The Four Execution Frontiers (IDNA Core)

IDNA identifies four runtime frontiers: * Identity (Who): Who is acting, with what authority, in what context. * Data (What): What information is admissible, trustworthy, and in scope for action. * Networks (Where/When): Whether execution conditions are stable, observable, and integrity-verified. * AI (How): What autonomous reasoning/decision logic is allowed to execute and how outputs are admitted.

These frontiers are coupled: failure in one compromises system safety, regardless of correctness in the others.

5. IDNA Runtime Control Topology

The IDNA frontiers function as a gating topology. Execution only proceeds when all four fronts agree.

graph TD
    %% Inputs
    In[Request / Signal / Intent] --> ID_Gate
    In --> DA_Gate
    In --> NW_Gate
    In --> AI_Gate

    %% Gates
    subgraph Frontiers [IDNA Joint Admissibility Boundary]
        ID_Gate["Identity Gate<br/>(Authority · Role · Context)"]
        DA_Gate["Data Gate<br/>(Admissibility · Integrity · Bounds)"]
        NW_Gate["Network Gate<br/>(Stability · Integrity · Telemetry)"]
        AI_Gate["AI Gate<br/>(Allowed Reasoning · Scope)"]
    end

    %% Core
    ID_Gate --> Core[Observer / AI Authority<br/>Invariant Enforcement · Admission · Refusal]
    DA_Gate --> Core
    NW_Gate --> Core
    AI_Gate --> Core

    %% Outputs
    Core --> Out_Admit["✅ Admissible Action / Advice"]
    Core --> Out_Refuse["❌ Refusal (with reason)"]
    Core --> Out_Audit["📜 System of Record (audit)"]

    %% Styling
    classDef gate fill:#061a3d,stroke:#155eef,stroke-width:1.5px,color:#fff;
    classDef core fill:#0b3d91,stroke:#f5a623,stroke-width:2px,color:#fff;
    classDef input fill:#f7fbff,stroke:#d8e6f7,color:#0a2146;
    classDef output fill:#f7fbff,stroke:#d8e6f7,color:#0a2146;
    class ID_Gate,DA_Gate,NW_Gate,AI_Gate gate;
    class Core core;
    class In input;
    class Out_Admit,Out_Refuse,Out_Audit output;

6. IDNA Outcomes (Outcome-Oriented)

These outcomes are phrased to be testable, auditable, and implementation-agnostic.

6.1 Identity Outcomes

• ID-OUT-01: Actions are attributable to an authenticated and authorized principal (human or non-human).
• ID-OUT-02: Context constraints (role, jurisdiction, scope) are evaluated prior to autonomous execution.
• ID-OUT-03: Identity binding influences admissibility (what questions/actions are allowed), not only access.

6.2 Data Outcomes

• DA-OUT-01: Data inputs influencing execution are verified for integrity, provenance, and scope.
• DA-OUT-02: Constraints (thresholds/ceilings/refusal rules) are encoded as enforceable data and applied deterministically.

6.3 Networks Outcomes

• NW-OUT-01: Runtime integrity signals are monitored continuously (e.g., anomaly indicators) independent of semantic reasoning.
• NW-OUT-02: Unsafe conditions trigger suppression/lockdown/refusal pathways before model output becomes admissible.

6.4 AI Outcomes

• AI-OUT-01: Model outputs are treated as hypotheses until admitted by an enforcement authority (Observer).
• AI-OUT-02: The system can refuse, qualify, or constrain outputs when invariants or conditions are violated.
• AI-OUT-03: All admissions/refusals are recorded with sufficient causal evidence for replay and examination.

7. Practical Applications, Examples, and Safety Impact

7.1 Practical Applications of the IDNA Framework

IDNA is intentionally domain-agnostic. It does not prescribe models, vendors, or industries; instead, it governs how autonomous computation is allowed to act.

1. Clinical & Medical AI (e.g., NovaMedX)

• Problem: Clinical AI failures arise when advice is generated without proper patient context, polluted data influences decisions, unstable networks go unnoticed, or probabilistic outputs are treated as facts.
• IDNA Application: Identity binds the request to a patient and clinician role; Data enforces admissibility (refusing if required labs are missing); Networks verify signal integrity; AI executes only within bounded authority via the Observer.
• Result: The system produces audit-ready clinical facts. Hallucinations are prevented not by "better prompts," but by mandatory refusal under unsafe conditions.

2. Autonomous Agents & Tool-Using AI

• Problem: Agentic systems fail when they act on behalf of the wrong principal, chain actions using stale data, or execute actions beyond delegated scope.
• IDNA Application: Identity defines delegation scope; Data constrains tool/API/dataset scope; Networks ensure execution conditions are stable; AI limits reasoning depth and side-effects.
• Result: Agents become governed executors. Unauthorized tool use becomes structurally impossible.

3. Safety-Critical Systems (Aviation, Industrial Control, Robotics)

• Problem: Safety systems integrate AI-driven decision logic without equivalent runtime governance.
• IDNA Application: Identity binds control authority; Data encodes safety envelopes; Networks function as "nerves" detecting pre-execution anomalies; AI reasoning is admitted only when all constraints are satisfied.
• Result: IDNA mirrors physiological safety systems (sensing ➔ constraint ➔ enforcement ➔ record). AI does not replace safety—it operates inside it.

4. Enterprise & Regulatory AI Governance

• Problem: Most governance frameworks are retrospective rather than runtime-enforced.
• IDNA Application: IDNA turns governance into execution reality (Identity = accountability, Data = enforceable law, Networks = operational readiness, AI = bounded authority).
• Result: Post-incident audit questions are answered deterministically with cryptographic evidence.

7.2 IDNA in Action — Concrete Examples

• Example 1: Clinical Query with Missing Data
  • Scenario: A clinician requests medication guidance, but required lab values are missing.
  • IDNA Behavior: Identity (Valid clinician + patient context ✅), Data (Required lab data missing ❌), Networks (Stable ✅), AI (Hypothesis generation allowed, but admission denied).
  • Outcome: The Observer refuses definitive advice and returns a qualified response explaining exactly which data is missing.
• Example 2: Prompt Injection Attempt
  • Scenario: An external system injects high-entropy instructions into an agent workflow.
  • IDNA Behavior: Identity (Source identity untrusted ❌), Networks (Entropy spike detected ❌), Data / AI (Not evaluated).
  • Outcome: Execution halts before semantic interpretation. No model reasoning occurs.
• Example 3: Autonomous Agent Overreach
  • Scenario: An AI agent attempts to execute a financial transaction beyond its delegated authority.
  • IDNA Behavior: Identity (Delegation scope exceeded ❌), AI (Action outside allowed reasoning boundary ❌).
  • Outcome: Action refused, violation logged with full causal chain.

8. Normative Requirements

8.1 General Requirements

• IDNA-GEN-01: The system SHALL evaluate Identity, Data, Networks, and AI frontiers prior to admitting any autonomous output or action.
• IDNA-GEN-02: The system SHALL treat model outputs as hypotheses until admitted by an enforcement authority.
• IDNA-GEN-03: If an invariant violation is detected, the system SHALL execute an intervention (refusal, qualification, constraint, or disengagement). Detection without enforcement SHALL be treated as system failure.
• IDNA-GEN-04: The system SHALL generate an examiner-safe record of admissions and refusals sufficient for forensic replay.

8.2 Frontier Requirements

• IDNA-I-01: The system SHALL bind each autonomous action to an authenticated principal (human or non-human) and an explicit authority context (role, scope, jurisdiction where applicable).
• IDNA-I-02: The system SHALL reject or refuse autonomous execution if authority context is absent, unverifiable, or out of scope.
• IDNA-I-03: The system SHOULD support delegated authority with traceable "on-behalf-of" chains for agentic operation.
• IDNA-D-01: The system SHALL validate that data used for autonomous execution is admissible (in-scope, integrity-verified, and appropriate to context).
• IDNA-D-02: Constraints (thresholds, ceilings, refusal criteria) SHALL be encoded as enforceable data structures and applied deterministically.
• IDNA-D-03: When required data is missing or invalid, the system SHALL refuse or qualify outputs according to defined constraints.
• IDNA-N-01: The system SHALL monitor runtime integrity signals sufficient to detect unsafe execution conditions (e.g., instability, anomalies, or integrity compromise) independent of semantic reasoning.
• IDNA-N-02: When unsafe conditions are detected, the system SHALL halt, constrain, or refuse autonomous execution prior to output admission.
• IDNA-N-03: The system SHOULD log integrity signals and the enforcement decision to support audit and replay.
• IDNA-A-01: The system SHALL implement an enforcement authority ("Observer") that is the sole component permitted to admit outputs/actions into the system-of-record.
• IDNA-A-02: The enforcement authority SHALL apply invariant checks and either admit, refuse, or qualify outputs before release.
• IDNA-A-03: The system SHALL record: (a) the output/action considered, (b) the applicable constraints, (c) integrity/condition signals, and (d) the admit/refuse decision.
• IDNA-A-04: The system MAY support graded autonomy (e.g., limited tool access, restricted action sets) based on risk tolerance and measured conditions.

9. Relationship to NIST AI RMF

IDNA is a reference model that complements the NIST AI Risk Management Framework (AI RMF). The table below maps each IDNA frontier to the NIST AI RMF Core functions and representative Playbook outcome areas.

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