# Risk Templates and Data Injection APIs

#### **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

***

#### **7.2.2 What Is a Risk Template?**

A **Risk Template** is a modular simulation package including:

| Component             | Description                                          |
| --------------------- | ---------------------------------------------------- |
| `TemplateID`          | Unique namespace (e.g., `FloodRisk@3.1`)             |
| `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.

***

#### **7.2.3 Example: Flood Risk Template**

```json
{
  "template_id": "FloodRisk@3.1",
  "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 `FloodRelief@3.2` integrate simulation outputs without coupling to a specific model implementation.

***

#### **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`

***

#### **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**.

***

#### **7.2.6 Data Provenance Metadata**

Injected data must include:

```json
{
  "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.**

***

#### **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.

***

#### **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.

***

#### **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.

***

#### **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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