> For the complete documentation index, see [llms.txt](https://docs.therisk.global/organization/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.therisk.global/organization/standardization/nexus-sovereignty/vii.-simulation-and-foresight/risk-templates-and-data-apis.md). # Risk Templates and Data APIs ## Standardized Simulation Packages, Authenticated Inputs, Model Registry Lineage, Verifiable Execution, and Clause-Compatible Risk Outputs ### Why Standardization Is Critical for Risk Modeling Risk modeling in the Nexus Sovereignty Framework cannot depend on informal model integrations, undocumented scripts, ad hoc data feeds, proprietary black boxes, or dashboard-specific outputs. Simulations in Nexus may affect Smart Clause activation, credential status, public-safe review, AI agent permissions, Project SPV evidence records, finance-readiness evidence, insurance-readiness evidence, emergency evidence routing, cross-jurisdictional coordination, and CAC generation. If every model exposes different inputs, different outputs, different assumptions, different uncertainty formats, and different provenance rules, then clauses cannot rely on simulations consistently, Credential Oracles cannot validate model dependencies, CAC runtimes cannot reproduce execution conditions, and governance bodies cannot compare outcomes across domains. Standardization is therefore a governance requirement, not merely a technical convenience. A flood model, public health model, climate scenario model, cyber contagion model, food security model, infrastructure outage model, financial exposure model, insurance basis-risk model, or AI agent stress-test model may differ scientifically, but each must expose enough common structure to be verifiable, auditable, reusable, forkable, and clause-compatible. The runtime must know what data was injected, where it came from, who signed it, which jurisdiction it applies to, which model version consumed it, which parameters were used, which uncertainty profile applies, which outputs were produced, and which governance function accepted or rejected the run. The Scenario Modeling Framework therefore defines **Risk Templates** and **Data Injection APIs**. A Risk Template is a governed simulation package that standardizes how a model declares its inputs, outputs, assumptions, parameters, uncertainty bounds, jurisdictional scope, runtime requirements, proof profile, and clause-binding interface. A Data Injection API is the controlled mechanism through which credentialed data providers, oracles, sensors, registries, and evidence systems deliver real-time or historical inputs into a verifiable simulation workflow. The core doctrine is: **A simulation cannot support high-consequence Nexus governance unless its model, inputs, outputs, provenance, execution environment, uncertainty, and clause interface are standardized enough to be verified by machines and reviewed by institutions.** ### Risk Templates as Governance Interfaces, Not Model Ownership A Risk Template should not be confused with ownership of a model or central control over scientific methods. The template does not force every jurisdiction or domain to use one global model. It defines the interface through which diverse models can become interoperable with Nexus clauses, credentials, CACs, registries, and governance records. A national hydrological model, university epidemiological model, enterprise infrastructure model, community vulnerability model, open-source climate model, or proprietary hazard model may all be integrated if they satisfy the template requirements for their declared scope. Templates preserve diversity while preventing chaos. They allow different model implementations to produce standardized outputs under known assumptions. They allow clause authors to bind to model families without hard-coding every implementation. They allow Simulation Governance Functions to approve, restrict, fork, or supersede templates. They allow auditors to compare model runs across time. They allow jurisdictions to adapt templates without breaking lineage. They allow Project SPV evidence workflows to reuse hazard, exposure, and monitoring templates while preserving confidentiality. The goal is not one model for all governance. The goal is a shared protocol for verifying many models. ### What a Risk Template Contains A Risk Template is a modular simulation package registered in the Model Registry. It should include model identity, template ID, model family, version, hash, input schema, output schema, parameter schema, scenario requirements, uncertainty model, jurisdictional scope, domain scope, accepted runtime profiles, proof requirements, governance status, review history, clause compatibility, public-safe boundary, and non-meaning fields. A mature template should specify: The **input schema**, including required and optional inputs, data types, coordinate systems, temporal resolution, spatial resolution, allowed formats, minimum data quality, accepted provenance classes, and privacy classifications. The **parameter schema**, including thresholds, calibration parameters, scenario assumptions, jurisdictional parameters, policy levers, and default values. The **execution profile**, including whether the template may run in TEEs, zkVMs, sovereign compute environments, enterprise evidence rooms, edge environments, or controlled research environments. The **output schema**, including risk scores, affected areas, confidence values, uncertainty bands, scenario classes, threshold flags, public-safe labels, and clause-compatible outputs. The **governance profile**, including model status, Simulation Governance review, recognized jurisdictions, review expiry, fork lineage, and dispute status. The **clause binding profile**, including which clause families may consume the output, which thresholds are supported, what CAC evidence must be produced, and what fallback applies if the model fails. The **boundary profile**, including explicit statements that outputs are evidence support, not official warning, public authority action, finance approval, insurance underwriting, legal determination, or treaty enforcement. A Risk Template therefore functions as a governance interface between scientific models and executable policy logic. ### Example Risk Template: Flood Risk Evidence A flood risk template may standardize how rainfall forecasts, soil saturation, river gauge data, elevation models, historical flood events, land cover, drainage infrastructure, and exposure data are transformed into clause-compatible risk outputs. A template record may look like: ```json { "template_id": "FloodRiskEvidence@3.1", "model_family": "FloodRisk", "model_hash": "0x23ab", "status": "active-limited", "input_schema": { "rainfall_forecast": { "format": "GeoTIFF", "required": true, "temporal_resolution": "hourly-or-daily", "provenance_required": true }, "soil_saturation": { "format": "netCDF", "required": true, "provenance_required": true }, "river_gauge_observations": { "format": "JSON-LD", "required": false, "provenance_required": true }, "historical_events": { "format": "JSON-LD", "required": true, "provenance_required": true } }, "forecast_schema": { "risk_score": "float", "affected_area": "GeoJSON", "confidence": "float", "uncertainty_band": "object", "threshold_flag": "boolean", "public_safe_class": "enum" }, "jurisdiction_scope": ["IND", "BD"], "runtime_profile": ["TEE", "controlled-sovereign-runtime"], "clause_compatibility": [ "FloodEvidenceRouting@3.*", "FloodPublicSafeSummary@1.*" ], "approved_by": [ "RegionalFloodSimulationGovernance" ], "created": "2025-04-01", "non_meaning": [ "not-official-warning", "not-relief-approval", "not-finance-approval", "not-insurance-underwriting" ] } ``` This template allows clauses such as `FloodEvidenceRouting@3.2` to consume flood risk outputs without depending on one hard-coded model implementation. One jurisdiction may use a national hydrological model. Another may use a regional flood model. A Project SPV evidence room may use a project-specific exposure extension. As long as the model conforms to the template, declares provenance, and passes governance review, its outputs can be interpreted by compatible clauses. The template standardizes the interface. It does not claim the model is universally valid. ### Data Injection APIs: Purpose and Design Data Injection APIs define how real-time, historical, synthetic, controlled, and credentialed data enter the simulation environment. This is one of the highest-risk parts of the Simulation Layer because bad inputs can make a good model produce unsafe outputs. Data poisoning, stale feeds, misclassified jurisdiction, unauthorized sensor data, unverified satellite imagery, manipulated field reports, and incompatible historical datasets can all corrupt clause decisions. A Data Injection API should therefore validate both the data and the authority of the data provider. It should check schema, format, timestamp, source identity, signer credential, jurisdiction, data classification, spatial scope, temporal scope, quality flags, consent or access restrictions, hash commitments, and permitted use. It should reject, quarantine, or route to review any input that fails provenance checks. A standard injection workflow should: Receive the data or data reference. Validate format and schema. Verify provider credential. Check source authorization and jurisdiction. Generate or verify data hash. Attach provenance metadata. Apply privacy and public-safe classification. Bind the input to a simulation run. Inject it into the approved runtime. Record input commitment in the Audit Layer. Return a structured acknowledgement or rejection. This makes the API a governance gate, not just a technical endpoint. ### Input Source Types Supported The SMF should support many input source types, but every source type must carry provenance, scope, and reliability metadata. Supported sources may include satellite and Earth observation data, weather forecasts, hydrological gauges, IoT and sensor feeds, SCADA or critical infrastructure telemetry, public health reports, epidemiological surveillance data, logistics and supply chain feeds, environmental monitoring data, market and macroeconomic indicators, financial exposure data, insurance exposure evidence, asset telemetry, project monitoring data, geospatial layers, historical disaster records, field observations, citizen science submissions, community-reported data, controlled evidence-room records, AI-generated synthetic data, model-generated scenario data, and official public datasets where lawfully available. Different source types require different controls. A satellite image may need geospatial provenance and collection time. A public health record may need privacy-preserving aggregation and legal access basis. A community observation may need community steward review. A critical infrastructure feed may need security classification. A Project SPV telemetry feed may need controlled evidence-room access. A synthetic dataset must be clearly labeled as synthetic and should not be treated as empirical observation. All inputs should be cryptographically timestamped where feasible and accompanied by provenance metadata. Sensitive payloads should not be exposed publicly merely because the input commitment is anchored. ### Data Provenance Metadata Injected data must include machine-readable provenance metadata. Provenance is what allows the simulation system to distinguish observed data, model-derived data, synthetic data, community-submitted data, enterprise evidence, public-sector records, and third-party feeds. A provenance record may look like: ```json { "source": "EarthObservationProvider-LandsatRef", "source_type": "EO-Optical", "hash": "0xabc123", "signed_by": "did:nsf:org:EarthObservationProvider", "provider_credential": "InputProviderVC#0x441", "collected_at": "2025-07-12T06:00:00Z", "ingested_at": "2025-07-12T06:14:00Z", "jurisdiction": "EGY", "spatial_scope": "NileDelta", "data_classification": "restricted-metadata-public", "permitted_use": [ "flood-risk-evidence-support", "simulation-run" ], "prohibited_use": [ "official-warning", "relief-approval", "insurance-underwriting" ] } ``` The seed used NASA as direct signer. Unless a direct authorized integration exists, safer examples should use reference-style identifiers such as `EarthObservationProvider-LandsatRef`, `PublicWeatherDataRef`, or `NationalHydrologyNode`, or state “where the provider has issued a valid credential.” This avoids implying institutional participation where none has been formally established. Provenance metadata should support downstream verification by Credential Oracles, Simulation Governance, CAC runtimes, Project Evidence reviewers, public-safe reviewers, and auditors. ### Templated Simulation Execution Workflow A templated simulation workflow begins when a clause, governance function, AI agent policy, Project Evidence workflow, or upgrade process requires a simulation. The system resolves the Risk Template ID and determines required inputs. Credentialed providers inject data through the Data Injection API. The API validates provenance and binds input commitments. The simulation runs in an approved execution environment. The output is structured according to the template’s forecast schema. A SimulationRunVC or equivalent attestation is issued. The output and proof record are linked to the clause, CAC, registry, or governance proposal. The Audit Layer records the full trace. A typical flow is: A clause declares that `FloodRiskEvidence@3.1` is required. The Model Registry resolves the template and active model versions. The required input schemas are loaded. Credentialed input providers submit rainfall, soil saturation, gauge, and historical event data. Input provenance is checked and committed. The simulation executes in a TEE, zkVM, sovereign runtime, controlled evidence room, or other approved environment. Outputs are validated against forecast schema. Uncertainty and public-safe labels are attached. A SimulationRunVC is issued. The clause receives a risk score, threshold flag, confidence value, and audit pointer. The CAC records the simulation reference and proof envelope. This entire pipeline must be traceable. If any input later becomes disputed, downstream simulation runs and CACs can be identified. ### Credentialed Data Injection Only credentialed providers should inject data into clause-sensitive templates. A public API can accept low-risk public inputs for exploratory analysis, but simulations that affect clauses, credentials, CACs, Project Evidence records, public-safe outputs, or governance decisions require credentialed input. An InputProviderVC should define provider identity, authorized domains, data types, jurisdictional scope, permitted templates, validity window, revocation status, Trust Anchor, data quality obligations, privacy obligations, and audit responsibilities. A credential may look like: ```json { "type": "InputProviderVC", "subject": "did:nsf:org:RegionalHydrologyDataNode", "authorized_domains": [ "FloodRisk", "HydrologyEvidence" ], "authorized_templates": [ "FloodRiskEvidence@3.*" ], "scope": { "jurisdiction": ["EGY", "SDN"], "data_types": ["river_gauge", "rainfall_forecast", "soil_saturation"] }, "issued_by": "did:nsf:org:RegionalWaterEvidenceRegistry", "valid_until": "2026-01-01T00:00:00Z", "revocation_registry": "CredentialStatusRegistry::InputProviders@1.0" } ``` The seed references WMO, WHO, and UNCTAD as Trust Anchors. Unless those relationships are formalized, use neutral trust anchors such as `RegionalWaterEvidenceRegistry`, `PublicHealthEvidenceRegistry`, `TradeLogisticsEvidenceRegistry`, or “competent authorized body where applicable.” Nexus can be compatible with multilateral data standards and official public datasets without implying endorsement or credential issuance by those institutions. Credentialed injection ensures that data authority is as verifiable as model authority. ### External Dataset and Oracle Validation Many simulations depend on external datasets. Some datasets will be official public datasets. Some will be commercial feeds. Some will be community-reported. Some will be generated by sensors. Some will be AI-derived. Some will be historical. Some will be controlled. The Data Injection API should not assume that all external data is equally trustworthy. Oracles can validate external datasets by checking source identity, signatures, checksums, collection time, licensing or access status, data freshness, schema compatibility, jurisdiction, anomalies, prior dispute status, and known quality flags. They can compare multiple sources where required. They can quarantine inconsistent feeds. They can issue provenance attestations. An oracle should not certify that the data is true. It should attest to provenance, integrity, freshness, schema compatibility, and declared source. Data truth remains a matter of evidence quality, validation, and governance review. This prevents provenance systems from overclaiming. ### Interoperability and Template Reuse Risk Templates should be reusable and forkable. A template can be adapted to new jurisdictions, new input channels, improved models, different runtime environments, or local governance constraints. A flood template may be forked for monsoon basins, urban drainage systems, coastal storm surge, or mountain catchments. A public health template may be extended with wastewater data. A climate exposure template may be adapted for a specific infrastructure asset class. A Project SPV evidence template may add asset telemetry and safeguard evidence. An AI agent stress-test template may add domain-specific tool misuse scenarios. Reuse must preserve lineage. A fork should record parent template, changed inputs, changed output schema, changed assumptions, changed jurisdiction, changed validation status, and compatibility notes. If a fork changes output meaning, existing clauses should not consume it automatically. If a fork only adds optional inputs while preserving required outputs, compatibility may be easier. Templates may also run in ensembles. Multiple model implementations can satisfy the same template and be compared for robustness. Clause triggers may require consensus, median output, conservative thresholding, disagreement detection, or human review when models diverge. Interoperability is not sameness. It is controlled compatibility. ### Model Registry and Template Governance Risk Templates should be stored in the Model Registry. The registry should record template ID, version, hash, status, governance review, fork lineage, compatible clauses, allowed runtimes, required inputs, output schema, uncertainty profile, approved jurisdictions, public-safe restrictions, and audit links. Template governance may be performed by Simulation Governance Functions, standards cells, domain experts, national nodes, regional consortia, community stewards, enterprise evidence governance, or DAO-compatible bodies. The governance function should review whether the template is scientifically appropriate, technically interoperable, privacy-aware, public-safe, and clause-compatible. Template status may include draft, under review, active, active-limited, advisory, restricted, deprecated, superseded, quarantined, or revoked. A template may be active for one jurisdiction and restricted in another. It may be accepted for evidence support but not for public-facing outputs. It may be recognized for Project Evidence but not for regulatory reporting. Registry governance prevents template reuse from becoming uncontrolled propagation. ### Risk Templates for AI Governance AI governance requires specialized templates for agent behavior, model-risk simulation, prompt injection, retrieval failure, tool misuse, memory leakage, role confusion, hallucinated authority, public-safe output, and cross-jurisdictional data access. These templates should define input prompts, tool environments, test suites, model versions, evaluator criteria, output classifications, risk metrics, and public-safe review requirements. An AI Agent Risk Template may produce outputs such as tool misuse score, restricted data leakage flag, public-safe violation class, memory boundary failure, supervisor override attempt, and recommended permission state. These outputs may support ModelStatusVCs, AgentToolUseVCs, MemoryPolicyVCs, and AI agent runtime restrictions. Such templates should never be treated as proof that an AI system is safe in all contexts. They support bounded governance decisions under declared test conditions. ### Risk Templates for Project SPVs, Finance-Readiness, and Insurance-Readiness Project SPV evidence workflows can benefit from standardized templates for asset exposure, climate hazard, operational resilience, safeguard evidence, monitoring reliability, data completeness, and basis-risk evidence. A Project Evidence Risk Template may accept asset location, design specifications, hazard layers, monitoring feeds, maintenance records, climate scenario pathways, and safeguard records. It may output evidence completeness, exposure class, monitoring confidence, scenario sensitivity, and public-safe summary fields. Finance-readiness templates can support evidence completeness, risk documentation, capital-readability, scenario traceability, and diligence package structure. They must not approve financing, provide investment advice, issue ratings, place securities, or guarantee capital. Insurance-readiness templates can support exposure evidence, monitoring continuity, hazard model traceability, basis-risk analysis, and claims-evidence preparedness. They must not underwrite, price risk, bind coverage, determine claims, or certify insurability. These templates make project evidence more structured and verifiable while preserving the boundary between public-good evidence infrastructure and regulated execution. ### Privacy, Security, and Community Data Controls Data injection can create privacy, security, and sovereignty risks. The template system must support field-level classification, restricted inputs, encrypted payloads, compute-to-data, SDZ execution, community-controlled records, enterprise evidence rooms, and public-safe output filters. Sensitive data should not move into generic runtimes where compute-to-data is required. Community-governed data should not be injected without community steward authorization. Health data should use privacy-preserving aggregation and lawful access controls. Critical infrastructure telemetry should be protected. Project SPV confidential evidence should remain in controlled rooms. Public-chain anchoring should use commitments, not raw data. A template should state whether inputs may be public, restricted, confidential, community-controlled, sovereign-controlled, enterprise-controlled, ZK-only, audit-only, or never-disclose. Output schemas should also carry public-safe classifications. Standardization must protect data, not flatten it. ### Boundary Statement for Risk Templates and Data Injection APIs Risk Templates and Data Injection APIs support standardized simulation packages, authenticated inputs, model registry lineage, provenance verification, clause-compatible risk outputs, SimulationRunVC generation, CAC compatibility, AI agent governance, Project SPV evidence workflows, finance-readiness evidence, insurance-readiness evidence, public-safe review, cross-jurisdictional interoperability, and audit-ready foresight infrastructure. They do not by themselves create legal authority, public authority status, regulatory approval, certification, procurement approval, finance approval, investment advice, insurance underwriting, claims determination, official public warning status, treaty enforcement, professional licensing, sovereign consent, community consent, data truth, model correctness, prediction certainty, or guaranteed outcomes. A template defines a verifiable interface for simulation. An injected dataset proves provenance and integrity under declared metadata. The institutional meaning of outputs depends on model scope, governance review, clause binding, credential status, jurisdiction, applicable law, contracts, community rules, and competent adoption. A template is not a standard imposed on all jurisdictions. A data feed is not truth. A provenance signature is not scientific validity. A risk score is not an official warning. A finance-readiness template is not finance approval. An insurance-readiness template is not underwriting. A Project Evidence template is not procurement approval. A multilateral reference dataset is not endorsement unless formally authorized. This boundary should appear in template records, Model Registry entries, Data Injection API documentation, SimulationRunVCs, CAC records, Project Evidence records, AI agent policies, public-safe outputs, and dashboards. ### Simulation Templates as Shared Foresight Infrastructure Risk Templates and Data Injection APIs transform simulation from bespoke modeling into reusable foresight infrastructure. They allow model developers to integrate with Nexus without surrendering scientific diversity. They allow clause authors to bind to known output formats. They allow CAC runtimes to verify live inputs. They allow Credential Oracles to check provider authority. They allow Simulation Governance Functions to review model scope. They allow Project SPV evidence rooms to structure scenario evidence. They allow public-safe reviewers to understand output meaning. They allow jurisdictions to fork and adapt templates without breaking audit lineage. This is the foundation for a global simulation protocol that remains sovereign, plural, and correction-ready. Models become verifiable. Inputs become provenance-bound. Outputs become clause-compatible. Forks remain traceable. Data remains jurisdiction-scoped. Sensitive records remain protected. AI risk tests become reusable. Project evidence becomes more structured. Finance-readiness and insurance-readiness evidence become clearer without crossing regulated boundaries. Risk Templates make simulation interoperable. Data Injection APIs make simulation trustworthy enough to govern with. Together, they turn the Simulation and Foresight Layer into shared public foresight infrastructure: machine-verifiable, institutionally reviewable, privacy-aware, forkable, and reusable across risk domains without centralizing science, authority, or data. --- # Agent Instructions This documentation is published with GitBook. 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