Risk Templates and Data Injection APIs

Standardized Interfaces for Scenario Modeling, Real-Time Forecasting, and Clause-Aware Simulations

7.2.1 Why Standardization Is Critical for Risk Modeling

In NSF, simulations must be:

• Clause-compatible

• Auditable and reproducible

• Composable across domains (climate, finance, health, etc.)

• Capable of live input ingestion and historical replay

• Verifiable within CAC and DAO-bound environments

To support this, NSF defines a template-based simulation schema with risk-specific injection APIs, allowing:

• Model developers to plug into the NSF protocol

• DAO and clause authors to reuse tested model templates

• CAC runtimes to simulate with authenticated real-time data

• Oracles to validate provenance of external datasets

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7.2.2 What Is a Risk Template?

A Risk Template is a modular simulation package including:

Component	Description
TemplateID	Unique namespace (e.g., [email protected])
ModelHash	Cryptographic hash of the model code
InputSchema	Expected structure and types of simulation inputs
ForecastSchema	Output structure expected by clause or DAO
SimulationInterface	API or CLI spec for execution
Metadata	License, author, jurisdictions, domains, constraints

Templates are versioned, SimDAO-approved, and stored in the NSF Model Registry for clause binding and CAC execution.

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7.2.3 Example: Flood Risk Template

{
  "template_id": "[email protected]",
  "model_hash": "0x23ab...",
  "input_schema": {
    "rainfall_forecast": "GeoTIFF",
    "soil_saturation": "netCDF",
    "historical_events": "JSON-LD"
  },
  "forecast_schema": {
    "risk_score": "float",
    "affected_area": "GeoJSON",
    "confidence": "float"
  },
  "jurisdiction": ["IND", "BD"],
  "interface": "REST + CLI",
  "approved_by": ["SimDAO-Asia"],
  "created": "2025-04-01"
}

This standardizes how clauses like [email protected] integrate simulation outputs without coupling to a specific model implementation.

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7.2.4 Data Injection APIs: Purpose and Design

Risk Templates are executed via standardized APIs that:

• Accept real-time or historical input streams

• Validate data against required schema

• Sign and hash provenance metadata

• Inject the data into a verifiable execution environment (TEE or zkVM)

• Return a signed output bundle with SimulationRunVC

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7.2.5 Input Source Types Supported

Data Type	Examples	Accepted Formats
Meteorological	Rainfall, temperature, humidity	GeoTIFF, HDF5, CSV
Economic	CPI, inflation, trade indices	JSON, CSV, World Bank API
Health	Infection rates, ICU occupancy, vaccine coverage	HL7-FHIR, CSV
Environmental	Air quality, water levels, biodiversity loss	netCDF, OGC WMS
Mobility/Supply Chain	Port activity, shipping times, migration	JSON-LD, GTFS, AIS feeds
Simulated Synthetic Data	Forecast proxies for backtesting	JSON, Parquet

All inputs must be cryptographically timestamped and accompanied by data provenance metadata.

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7.2.6 Data Provenance Metadata

Injected data must include:

{
  "source": "NASA-EO-Landsat",
  "hash": "0xabc123...",
  "signed_by": "did:nsf:org:NASA",
  "collected_at": "2025-07-12T06:00Z",
  "jurisdiction": "EGY",
  "sensor_type": "EO-Optical"
}

This allows downstream simulation pipelines to verify source, jurisdiction, and relevance.

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7.2.7 Templated Simulation Execution Workflow

1. DAO signals clause requiring simulation

2. Risk Template ID and required inputs identified

3. Credentialed input providers inject data via API

4. Simulation run executed in TEE or zkVM

5. Output attested by SimulationRunVC

6. Clause receives structured risk output and threshold flag

This entire pipeline is logged to the Audit Layer.

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7.2.8 Credentialed Data Injection

Only credentialed oracles and data providers may inject data into clause-sensitive templates.

They must hold an InputProviderVC, defining:

• Authorized domains

• Expiration

• Trust anchor (e.g., WMO, WHO, UNCTAD)

• Scope (e.g., “Flood risk models in MENA region”)

Oracles validate data hashes before execution.

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7.2.9 Interoperability and Template Reuse

Templates can be:

• Forked by domain experts

• Extended with new input channels (e.g., adding air quality to health forecasts)

• Adapted to jurisdictional conditions

• Simulated in ensembles to test robustness of clause triggers

Each reuse maintains template lineage and is recorded in the Model Registry.

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7.2.10 NSF Simulation Templates as Global Risk Protocols

Risk Templates allow NSF to operate as a global API for policy simulation.

• Models are verifiable, forkable, composable

• Data is authenticated, jurisdiction-scoped, and real-time capable

• Clauses receive risk triggers in standardized formats

• CAC environments can simulate dynamically with ZK guarantees

• DAOs approve or reject outputs based on traceable input provenance

Templates turn simulations into shared public foresight infrastructure—verifiable by machine, enforceable by institution, and reusable across all domains.

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