# XXI. TECHNOLOGIES

## 21.1 AI and Agentic Systems

### 21.1.1 AI object intake.

AI object intake under the Distributed Digital Public Goods Framework shall apply to any model object, AI system object, AI-agent object, retrieval-augmented system, fine-tuned model, foundation-model interface, classifier, forecasting model, simulation model, digital twin model, risk model, optimization model, decision-support model, evaluation harness, prompt library, AI-assisted workflow, agentic workflow, model card, system card, benchmark card, or AI-enabled Studio workflow proposed for Nexus use, listing, publication, Registry recording, Grid review, TRL note, Academy use, Foundry build, Campaign use, Reports inclusion, Nexus Universe presentation, National Portfolio use, Observatory use, DRI use, or lawful handoff context.

AI object intake shall record the object identity, steward, source pathway, intended use, prohibited use, model class, data basis, training or configuration basis where known, retrieval sources where applicable, tool permissions where applicable, human oversight pattern, AI-use label, access class, sensitivity class, public-safe status, cyber status, privacy status, bias and harm review needs, security needs, evaluation needs, red-team needs, incident pathway, withdrawal pathway, support status, and correction pathway. No AI object shall proceed merely because it is technically impressive, commercially available, sponsor-supported, provider-contributed, or publicly popular. AI object intake shall preserve the rule that AI is an evidence-bearing object and workflow support layer, not an authority-bearing actor by default.

### 21.1.2 Model card.

A **Model Card** shall be the recorded public-good documentation object describing a model’s purpose, class, intended use, prohibited use, data provenance summary, training or configuration notes where disclosable, evaluation records, benchmark records, limitations, known risks, bias and harm considerations, human review requirements, public-safe status, support class, access class, license status, AI-use label, incident pathway, withdrawal pathway, version, steward, and correction pathway. Model Cards shall be required for model objects where meaningful external understanding, review, Registry recording, Marketplace listing, Studio use, Grid input, TRL note, or handoff context is expected.

A Model Card shall not be AI safety certification, model approval, general validation, public authority decision, procurement readiness, financeability, insurability, deployment authorization, medical approval, employment decision authorization, public warning authority, or execution authority. It is a bounded record of known information, limitations, and governance conditions.

### 21.1.3 System card.

A **System Card** shall document an integrated AI system or AI-enabled workflow, including model components, retrieval components, data sources, APIs, tools, user roles, human review points, output classes, no-command rules, no-write-back rules, access controls, logging, cyber controls, privacy controls, public-safe controls, evaluation records, red-team records, incident pathways, shutdown triggers, and correction mechanisms. System Cards shall be used where a model is embedded in a broader workflow, dashboard, Studio runtime, public authority learning workflow, readiness workflow, data-room workflow, secure-room workflow, Observatory workflow, DRI workflow, Academy pathway, Marketplace function, Registry workflow, or handoff package.

A System Card shall not authorize operation, deployment, automated decision-making, public authority action, procurement, finance, insurance, underwriting, community implementation, Indigenous-context implementation, or enterprise execution. The system remains bounded by its recorded use class and review status.

### 21.1.4 Agent workflow card.

An **Agent Workflow Card** shall document any AI-agent or agentic workflow that can plan, call tools, use APIs, retrieve data, generate code, alter files, initiate messages, update records, query databases, summarize materials, classify signals, prepare outputs, or participate in Studio, Academy, Foundry, Campaign, Marketplace, Registry, Reports, Observatory, DRI, Grid, TRL, or handoff workflows. The Agent Workflow Card shall identify agent identity, purpose, tool permissions, prohibited tools, data access, write permissions, no-command rules, no-write-back rules, human-in-the-loop requirements, human-on-the-loop requirements, logging, output review, escalation triggers, kill-switch conditions, incident pathways, support status, and correction pathway.

Agentic workflows shall default to controlled assistance and review support. Agent output shall not be a determination, public authority decision, procurement decision, finance decision, insurance decision, employment decision, consent decision, deployment instruction, operational command, or execution act by default.

### 21.1.5 AI-use label.

An **AI-Use Label** shall identify whether and how AI may be used with an object, including no-AI use, retrieval-only, summarization with review, classification with review, evaluation-only, fine-tuning permitted, training permitted, agentic use prohibited, secure-room AI only, public-safe AI output only, or other recorded AI-use class. AI-use labels shall be attached to data objects, model objects, learning objects, Reports, dashboards, Studio workflows, Marketplace listings, Registry records, DRI objects, Observatory objects, National Portfolio objects, and handoff packages where AI use could affect rights, safety, privacy, protected knowledge, security, or authority boundaries.

AI-use labels shall not create permission beyond recorded scope. A permissive label shall remain subject to license, data rights, privacy, sensitivity, protected knowledge, access class, jurisdictional controls, and public-safe review. An AI-use label shall not certify AI safety or authorize automated high-stakes decisions.

### 21.1.6 Red-team record.

A **Red-Team Record** for AI shall document adversarial testing, misuse testing, prompt-injection testing, data leakage testing, bias and harm testing, tool-permission testing, model behavior testing, jailbreak testing, hallucination testing, unsafe output testing, protected knowledge exposure testing, cyber misuse testing, public authority overclaim testing, finance overclaim testing, procurement overclaim testing, and deployment overclaim testing. Red-Team Records shall identify scope, methods at a safe level of detail, findings, severity, limitations, mitigations, unresolved risks, reviewer class, date, affected versions, correction pathway, and disclosure status.

Red-Team Records may be controlled or public-safe summarized where detailed disclosure could enable misuse. A red-team process shall not create AI safety certification, security certification, public authority approval, procurement readiness, financeability, insurability, deployment authorization, or execution authority.

### 21.1.7 Human oversight pattern.

Each AI object shall record the required human oversight pattern, including human-in-the-loop, human-on-the-loop, human review after generation, human approval before release, expert review, public-safe review, data steward review, AI steward review, cyber review, privacy review, safeguard review, legal-boundary review, public authority boundary review, or handoff review. The oversight pattern shall identify who reviews, what they review, when review occurs, what outputs require approval within scope, what outputs are prohibited, when escalation is required, and when shutdown or withdrawal applies.

Human oversight shall not be symbolic. AI objects shall not be used to bypass review, substitute for expert judgment, automate high-stakes decisions, issue public warnings, make public authority decisions, make finance or insurance decisions, rank persons or communities, or authorize deployment by default.

### 21.1.8 AI incident record.

An **AI Incident Record** shall document harmful, erroneous, unsafe, unauthorized, biased, discriminatory, privacy-invasive, security-compromising, protected knowledge-exposing, misleading, overclaimed, unreviewed, or boundary-violating AI behavior. Incidents may include hallucinated authority, unsafe public-safe summaries, incorrect classifications, unauthorized data use, prompt-injection compromise, tool misuse, unauthorized write-back, unauthorized command attempt, model leakage, re-identification risk, harmful recommendations, public authority overclaim, finance overclaim, procurement overclaim, consent overclaim, deployment overclaim, or execution overclaim.

AI Incident Records shall trigger containment, output withdrawal, access suspension, tool restriction, model withdrawal, Registry update, Marketplace delisting, Report correction, Studio shutdown, Grid or TRL downgrade, handoff recall, public-safe notice where appropriate, archive, and correction propagation. AI incidents shall be recorded as part of object memory.

## 21.2 AI-RAN, O-RAN, Telecom, Edge, and Private Wireless

### 21.2.1 Network object records.

Network object records shall document telecom, AI-RAN, O-RAN, private wireless, edge network, 5G-relevant, 6G-relevant, sensor-network, connectivity, routing, spectrum-dependent, network-resilience, and degraded-mode objects used or referenced within DDPGF. Records shall identify network object class, steward, provider context, architecture summary, interface profile, dependency records, access model, cyber controls, public authority dependencies, spectrum dependencies, resilience assumptions, data flows, edge-node linkages, Observatory linkages, Studio linkages, National Portfolio relevance, support status, and correction pathway.

Network object records shall not create telecom authorization, spectrum authorization, provider validation, public authority approval, procurement recommendation, deployment authorization, operational command, emergency communication authority, or execution authority by default.

### 21.2.2 Spectrum dependency notes.

Spectrum dependency notes shall record whether an object, demonstration, workflow, prototype, testbed, Observatory node, edge node, private wireless component, AI-RAN component, O-RAN component, sensor network, or handoff context depends on licensed, unlicensed, shared, experimental, public safety, industrial, private, or other spectrum arrangements. Notes shall identify the relevant dependency, jurisdictional context, public authority or regulator dependency, testing constraints, lawful-use constraints, provider context, and unresolved questions.

A spectrum dependency note shall not authorize spectrum use, create regulatory approval, grant a license, approve equipment, authorize deployment, or substitute for competent telecom regulatory processes.

### 21.2.3 Interoperability profiles.

Interoperability profiles shall describe how network objects interface with open interfaces, APIs, O-RAN components, edge systems, cloud systems, compute environments, sensor networks, data pipelines, DRI workflows, Observatory nodes, Studio workflows, dashboards, secure rooms, and handoff packages. Profiles shall include standards-interface references, versioning, compatibility notes, testing status, known limitations, provider-neutrality notes, security considerations, and correction pathways.

Interoperability profile status shall not create standards conformance certification, vendor approval, procurement readiness, public authority approval, deployment authorization, or operational fitness.

### 21.2.4 Edge node records.

Edge node records shall document local compute, storage, sensing, preprocessing, AI inference, connectivity, dashboard, Observatory, Studio, DRI, National Portfolio, or Nexus Universe edge capabilities. Records shall identify physical or logical location class, sensitivity, host, provider, steward, data flows, access model, AI-use label, cyber status, privacy status, public-safe status, degraded-mode behavior, support status, maintenance responsibilities, teardown rules, and archive rules.

An edge node record shall not create surveillance authority, local deployment authority, public authority action, emergency command, provider validation, community consent, Indigenous consent, or execution authority by default.

### 21.2.5 Cyber-resilience records.

Cyber-resilience records shall document the resilience posture of network objects, including segmentation, redundancy, failover, backup connectivity, low-bandwidth operation, degraded-mode awareness, secure access, vulnerability management, logging, monitoring, incident response, dependency exposure, public-safe cyber disclosure, and restoration pathways. Cyber-resilience records may support Studio demonstrations, Observatory workflows, DRI dashboards, National Portfolio readiness, and handoff context.

Cyber-resilience records shall not create cybersecurity certification, service guarantee, public authority approval, procurement readiness, deployment authorization, or operational assurance for all use cases.

### 21.2.6 Public authority dependency notes.

Public authority dependency notes shall record where telecom, AI-RAN, O-RAN, private wireless, edge, sensor, or network objects require public authority action, regulatory review, spectrum permission, infrastructure permission, public safety interface, emergency communications interface, municipal consent, utility coordination, or other public authority dependency. Notes shall identify dependency type, unresolved issues, authority boundary, non-decision status, and handoff implications.

Public authority dependency notes shall not create public authority approval or imply that DDPGF has authority to act on behalf of a regulator, public safety body, municipality, ministry, or infrastructure authority.

### 21.2.7 Provider-neutrality notes.

Provider-neutrality notes shall document provider dependencies, vendor components, open-interface posture, proprietary dependencies, cloud relationships, hardware dependencies, support dependencies, interoperability limitations, lock-in risks, provider contributions, sponsor support, and alternative options where feasible. Provider-neutrality notes shall preserve procurement neutrality, avoid vendor validation, and prevent sponsor or provider capture.

Provider-neutrality notes shall not certify provider neutrality in an absolute sense. They shall record known dependencies and boundaries for review, correction, and lawful handoff.

### 21.2.8 Handoff context.

Handoff context for telecom, AI-RAN, O-RAN, private wireless, edge, and network objects shall identify evidence context, technical context, interface context, spectrum dependencies, regulatory dependencies, public authority dependencies, cyber dependencies, data dependencies, provider-neutrality notes, support status, deployment constraints, procurement boundaries, finance and insurance questions, recipient responsibilities, correction pathways, and recall pathways.

Handoff context shall not authorize network deployment, spectrum use, public safety integration, infrastructure operation, provider selection, procurement, financing, insurance, community implementation, Indigenous-context implementation, operational command, or execution.

## 21.3 HPC, Cloud, Sovereign Compute, and Verifiable Compute

### 21.3.1 Compute environment records.

Compute environment records shall document cloud environments, compute clusters, HPC environments, sovereign compute environments, secure enclaves, edge compute, Studio runtime environments, data-room compute, AI evaluation environments, model training or inference environments, simulation environments, and compute-to-data environments. Records shall identify provider, jurisdiction, environment class, workload class, data class, AI-use class, access model, security controls, privacy controls, cost and support assumptions, logging, monitoring, continuity, teardown, and archive.

A compute environment record shall not create provider validation, public authority approval, deployment authorization, data rights, AI training permission, procurement status, financeability, insurability, or execution authority.

### 21.3.2 Workload classification.

Workload classification shall determine whether a compute workload is public, internal, controlled, restricted, secure-room-only, data-room-only, compute-to-data, public authority-sensitive, health-sensitive, youth-sensitive, community-sensitive, Indigenous protocol-sensitive where applicable, protected knowledge, cyber-sensitive, infrastructure-sensitive, geospatial-sensitive, model-sensitive, or handoff-recipient-only. Classification shall govern access, logging, output review, jurisdiction, compute environment, retention, deletion, archive, and incident response.

Workload classification shall not approve the workload for publication, deployment, finance, procurement, insurance, public authority use, or handoff by itself. It determines controls.

### 21.3.3 Secure enclave records.

Secure enclave records shall document isolated or protected compute environments used for restricted data, sensitive workloads, protected knowledge, AI evaluation, compute-to-data, model testing, confidential analytics, public authority-sensitive workflows, National Portfolio data, Observatory data, DRI data, or handoff packages. Records shall identify enclave purpose, architecture class, access model, key management where applicable, workload approval, user approval, logging, output review, incident response, teardown, and archive.

Secure enclave records shall not create security certification, public authority approval, data rights, provider validation, deployment authorization, or operational guarantee.

### 21.3.4 Compute-to-data workflows.

Compute-to-data workflows shall bring approved computation to controlled data rather than exporting raw data. They shall record approved workloads, approved users, query controls, output review, privacy-preserving methods, secure enclave use, confidential computing where appropriate, no raw data extraction, audit records, incident response, and archive rules.

Compute-to-data workflows shall not convert restricted data into open data, authorize AI training beyond scope, create cross-border transfer permission, grant data rights, create handoff permission, or authorize deployment.

### 21.3.5 Verifiable execution notes.

Verifiable execution notes shall document where cryptographic, procedural, audit, logging, reproducibility, provenance, attestation, hash, timestamp, proof receipt, secure enclave attestation, or other verifiable compute methods are used to strengthen trust in execution of a workload, build, analysis, model evaluation, data transformation, report generation, or public-safe output. Notes shall identify what is verifiable, what is not verifiable, evidence limits, dependencies, and correction pathway.

Verifiable execution notes shall enhance traceability but shall not create absolute truth, certification, legal proof for all purposes, public authority approval, security certification, financeability, insurability, procurement readiness, deployment authorization, or execution authority.

### 21.3.6 Cost and support records.

Cost and support records shall document compute cost assumptions, credits, sponsorship, in-kind support, funding dependencies, scaling costs, storage costs, egress costs, support status, maintainer responsibility, provider responsibility, renewal risk, discontinuation risk, exit cost, and archive cost. These records shall support sustainability, anti-capture, provider-neutrality, continuity, and handoff context.

Cost and support records shall not create budget approval, finance commitment, donor commitment, public finance allocation, procurement approval, warranty, service-level obligation, or execution duty.

### 21.3.7 Access-control records.

Access-control records shall document who can access compute environments, at what privilege level, for what purpose, under what authentication, for what duration, with what export rights, with what tool permissions, and with what revocation rules. Access-control records shall cover users, stewards, maintainers, reviewers, data stewards, AI stewards, public authority learning participants, capital readers, insurers, donors, providers, sponsors, National Nodes, Competence Cells, Studio users, and handoff recipients.

Access-control records shall not create entitlement, ownership, data rights, AI-use rights, public authority status, procurement status, or deployment authority.

### 21.3.8 Teardown and archive.

Compute teardown and archive shall close or retire environments safely by revoking access, rotating secrets, exporting approved records, deleting or sealing data where required, archiving logs where lawful, preserving correction records, updating Registry status, delisting Marketplace objects where needed, correcting Reports, closing Studio workflows, updating Grid or TRL notes, recalling handoff packages where appropriate, and terminating cost exposure.

Teardown and archive shall prevent abandoned compute, orphaned credentials, hidden data retention, unmanaged costs, stale outputs, and unsupported reliance. Archive shall preserve memory without current authority.

## 21.4 Cyber, Zero Trust, OT, IoT, and Critical Infrastructure

### 21.4.1 Cyber object intake.

Cyber object intake shall apply to vulnerability reports, threat models, SBOMs, dependency scans, secret scans, security configurations, access logs, incident records, red-team records, OT/IoT/IIoT records, critical infrastructure diagrams, zero-trust profiles, secure-room workflows, public-safe cyber summaries, and cyber-sensitive Reports or learning objects. Intake shall record object class, sensitivity, source, affected systems, disclosure status, access class, public-safe status, steward, review needs, incident pathway, correction pathway, and archive rule.

Cyber objects shall be restricted by default where publication could enable misuse. Intake shall not convert cyber objects into operational instructions, public warnings, certifications, procurement qualifications, or deployment approvals.

### 21.4.2 Zero-trust profile.

A **Zero-Trust Profile** shall document how a DDPGF object or environment applies identity verification, authentication, authorization, least privilege, segmentation, device or environment controls where appropriate, logging, monitoring, access recertification, anomaly review, export controls, and revocation. Zero-trust profiles may apply to secure rooms, data rooms, compute-to-data workflows, Studio workflows, APIs, repositories, Marketplace administration, Registry administration, and handoff packages.

A zero-trust profile shall not be cybersecurity certification, compliance determination, public authority approval, or guarantee that a system cannot be compromised.

### 21.4.3 OT / IoT sensitivity notes.

OT, IoT, and IIoT sensitivity notes shall identify cyber-physical risks, safety dependencies, infrastructure dependencies, device vulnerabilities, telemetry sensitivity, location sensitivity, command risks, network exposure, maintenance constraints, public authority dependencies, and public-safe disclosure limitations. These notes shall apply to sensor networks, edge nodes, Observatory nodes, digital twins, infrastructure dashboards, robotics, drones, and critical infrastructure-related workflows.

OT/IoT sensitivity notes shall not authorize connection to live systems, operational command, field intervention, public authority action, deployment, or execution.

### 21.4.4 Vulnerability disclosure object.

A **Vulnerability Disclosure Object** shall record a reported or discovered vulnerability, affected object, affected versions, severity, reproduction scope at a safe level of detail, reporter, steward, containment actions, mitigation, correction, disclosure status, Registry updates, Marketplace actions, Report corrections, Studio shutdowns, Grid or TRL updates, handoff recalls, and archive status. It may be controlled, secure-room-only, or public-safe summarized.

A vulnerability disclosure object shall not be exploit guidance, public warning, cybersecurity certification, legal determination, or deployment prohibition by default.

### 21.4.5 Red-team object.

A **Red-Team Object** shall record adversarial review of software, models, AI agents, APIs, dashboards, data workflows, cyber-physical systems, Studio workflows, public-safe outputs, or handoff packages. It shall identify scope, method summary, findings, severity, limitations, mitigation, retest needs, disclosure status, and correction pathway.

Red-team objects shall be controlled where detail could enable misuse. Red-team completion shall not create security certification, AI safety certification, procurement readiness, or deployment authorization.

### 21.4.6 Cyber incident object.

A **Cyber Incident Object** shall document unauthorized access, attempted access, credential compromise, secret exposure, vulnerability exploitation, API abuse, repository compromise, data exfiltration, model abuse, prompt injection, tool misuse, OT/IoT issue, secure-room breach, data-room misuse, compute-to-data misuse, or other cyber-relevant incident. It shall record detection, containment, severity, affected objects, affected users where appropriate, correction, notification, Registry update, Marketplace action, Report correction, handoff recall, and archive.

Cyber incident objects shall be handled according to privacy, security, legal, public-safe, and protected knowledge controls.

### 21.4.7 Public-safe cyber summary.

A **Public-Safe Cyber Summary** shall communicate cyber-relevant information safely, without enabling exploitation, exposing sensitive configurations, identifying vulnerable targets unnecessarily, disclosing credentials, revealing protected knowledge, or creating panic. It may describe status changes, mitigation, corrected versions, user actions where safe, and correction pathways.

Public-safe cyber summaries shall not be official public warnings, emergency commands, security certifications, or operational instructions unless separately issued by competent authority.

### 21.4.8 No operational command notice.

Cyber, OT, IoT, IIoT, critical infrastructure, sensor, edge, network, and Studio objects shall carry no operational command notices where users could mistake materials for instructions to operate, stop, start, modify, patch, connect, disconnect, deploy, or control live systems. The notice shall clarify that DDPGF materials support learning, review, public-safe reporting, readiness, and handoff context only.

Operational command requires competent system owners, operators, public authorities, or lawful actors outside DDPGF default posture.

## 21.5 Geospatial, Earth Observation, Digital Twins, Sensors, Drones, and Robotics

### 21.5.1 Geospatial layer records.

Geospatial layer records shall document source, scale, resolution, date, coordinate reference where appropriate, method, sensitivity, masking, aggregation, delay rules, access class, license, public-safe status, confidence, uncertainty, correction pathway, and archive rule for any spatial object. They shall apply to hazard layers, exposure layers, infrastructure layers, WFEH-B layers, biodiversity layers, sensor-location layers, National Portfolio maps, DRI maps, Observatory maps, and digital twin inputs.

A geospatial layer record shall not create official map status, legal boundary status, warning authority, public authority decision, rating, procurement signal, or deployment authorization.

### 21.5.2 Earth observation source notes.

Earth observation source notes shall document satellite, aerial, drone, radar, optical, thermal, LiDAR, multispectral, hyperspectral, atmospheric, hydrological, ecological, climate, weather, or oceanographic sources used to create digital public-good objects. Notes shall identify provider, source rights, resolution, date, processing method, uncertainty, confidence, sensitivity, public-safe transformation, masking, license, and correction pathway.

Earth observation source notes shall not create official determination, forecast certainty, public warning, surveillance authority, or deployment authorization.

### 21.5.3 Digital twin assumptions.

Digital twin assumption records shall document data basis, model basis, scenario basis, boundary conditions, update cadence, uncertainty, confidence, limitations, sensitivity, public-safe interpretation, no-command controls, no-write-back controls, public authority boundary, and correction pathway. Assumptions shall be visible wherever digital twin outputs could be overinterpreted.

A digital twin assumption record shall make clear that a digital twin is a learning and simulation object, not an operational command system, forecast certainty, public authority decision, or deployment authority by default.

### 21.5.4 Sensor and edge records.

Sensor and edge records shall document sensor class, edge node class, host, steward, location sensitivity, data stream, access controls, cyber controls, privacy status, calibration status where applicable, public-safe transformation, data-use label, AI-use label where applicable, retention, support status, incident pathway, and correction pathway.

Sensor and edge records shall not authorize surveillance, public warning, field operation, infrastructure operation, community monitoring, or deployment by default.

### 21.5.5 Drone and robotics constraints.

Drone and robotics constraints shall record whether a digital object, dataset, model, dashboard, Studio workflow, simulation, or handoff context involves drones, robotics, autonomous systems, remote operation, field sensing, imagery, mobility, manipulation, actuation, or cyber-physical control. Constraints shall identify safety, aviation, public authority, privacy, location, community, Indigenous protocol-sensitive where applicable, protected knowledge, cyber, operational, and handoff dependencies.

DDPGF shall not authorize drone flights, robotics operation, autonomous field activity, operational command, public authority action, or deployment by implication. Any such action requires competent lawful actors and separate authority.

### 21.5.6 Protected location controls.

Protected location controls shall apply to sensitive infrastructure, protected species, sacred sites, protected knowledge, community-sensitive locations, health-sensitive locations, humanitarian sites, youth-related sites, cyber-sensitive sites, and other locations where disclosure could create harm. Controls may include masking, aggregation, delay, controlled access, protected knowledge rooms, metadata-only release, and archive restrictions.

Protected location controls shall override ordinary map publication and open-data preferences where necessary.

### 21.5.7 Public-safe atlas outputs.

Public-safe atlas outputs shall present geospatial, Earth observation, Observatory, DRI, WFEH-B, National Portfolio, digital twin, and scenario materials in a safe, accessible, bounded, and non-authoritative form. Atlas outputs shall include source notes, update cadence, confidence and uncertainty labels, masking notices, sensitivity notes where safe, no-warning notices, no-rating notices, no-official-map notices, and correction pathways.

A public-safe atlas output shall not be an official map, public warning, country ranking, community ranking, insurance map, investment map, procurement map, operational command surface, or deployment tool by default.

### 21.5.8 Handoff dependency records.

Handoff dependency records for geospatial, Earth observation, digital twin, sensor, drone, and robotics objects shall identify legal dependencies, aviation dependencies where applicable, telecom dependencies, data dependencies, location-sensitivity dependencies, public authority dependencies, community safeguards, Indigenous protocols where applicable, protected knowledge restrictions, cyber dependencies, safety dependencies, provider dependencies, support status, and recipient responsibilities.

Such records shall not authorize implementation, field operation, drone operation, robotics operation, public authority action, procurement, finance, insurance, community implementation, or deployment.

## 21.6 DLT, Web3, DePIN, Digital Identity, and Verification Infrastructure

### 21.6.1 Ledger object records.

Ledger object records shall document any blockchain, distributed ledger, hash registry, timestamping mechanism, proof registry, decentralized infrastructure record, DePIN-relevant object, audit trail, verification record, or public-good ledger pattern used within DDPGF. Records shall identify purpose, ledger type, governance, data written, data not written, privacy controls, key management, immutability implications, correction method, reversibility limitations, cost, sustainability, jurisdictional context, and boundary notices.

Ledger object records shall not create financial instrument status, token authorization, investment product status, public authority approval, procurement approval, legal proof for all purposes, or execution authority.

### 21.6.2 Proof receipt patterns.

Proof receipt patterns shall document how DDPGF may issue, store, verify, reference, correct, revoke, or archive proof receipts for object existence, version, contribution, review, publication, learning, support, Registry status, Marketplace listing, Studio workflow, Grid input, TRL note, or handoff context. Proof receipts may use hashes, timestamps, signatures, attestations, repository identifiers, DOIs, or ledger records.

A Proof Receipt shall verify a recorded event or state within scope; it shall not certify quality, approve content, create professional credential, financeability, procurement status, public authority approval, consent, deployment authorization, or execution authority.

### 21.6.3 Identity verification objects.

Identity verification objects shall support controlled identity, account, role, contributor, maintainer, reviewer, learner, steward, public authority learning participant, secure-room user, data-room user, Studio user, or handoff-recipient verification. Records shall identify verification scope, privacy controls, retention, access class, consent, correction pathway, and revocation pathway.

Identity verification shall not create employment status, professional license, public authority status, procurement eligibility, finance status, immigration status, or execution authority by default.

### 21.6.4 Credential verification objects.

Credential verification objects shall support verification of micro-credentials, badges, learning records, WILP records, contribution records, reviewer standing, maintainer standing, Academy records, Risk Academy records, and ILA-linked records. They shall identify issuer, evidence basis, expiry, renewal, suspension, withdrawal, correction history, privacy status, display rules, and boundary notices.

Credential verification shall not create professional license, degree equivalence, employment eligibility, procurement qualification, public authority credential, deployment authorization, or execution authority by default.

### 21.6.5 Token, equity, and finance prohibition by default.

DDPGF shall prohibit treating contribution credits, proof receipts, badges, recognition records, ledger entries, support records, Campaign pledges, Marketplace signals, Registry records, or handoff notes as tokens, equity, securities, currencies, payment instruments, investment contracts, financial products, insurance products, claims on revenue, governance tokens, or tradable assets by default. Any regulated financial instrument would require separate competent legal structuring outside DDPGF public-good default posture.

This prohibition shall protect contributors, learners, communities, sponsors, providers, and the public-good stack from financialization by implication.

### 21.6.6 Key-management controls.

Key-management controls shall govern cryptographic keys, signing keys, ledger keys, access keys, proof receipt keys, identity keys, credential keys, secure-room keys, API keys, encryption keys, and recovery keys. Controls shall include key generation, storage, access, rotation, revocation, recovery, separation of duties, logging, incident response, and archive rules.

Key management shall not create absolute proof, security certification, public authority approval, or immunity from compromise. Key incidents shall trigger correction and revocation where appropriate.

### 21.6.7 Governance and correction records.

DLT, Web3, DePIN, identity, and verification objects shall include governance and correction records that address immutability, revocation, correction, supersession, withdrawal, recall, dispute, mistaken entries, privacy requests, data minimization, and archive. Where on-chain or immutable entries cannot be deleted, correction shall use revocation, supersession, annotated status, off-chain controls, sealing, or public-safe notices as appropriate.

Immutability shall not be used to defeat privacy, protected knowledge, correctionability, or lawful withdrawal where required.

### 21.6.8 No financial instrument by implication.

No DDPGF ledger object, proof receipt, identity object, credential object, contribution record, support record, recognition record, Marketplace signal, Registry record, Campaign record, handoff note, or verification object shall be interpreted as a financial instrument, security, token, derivative, currency, deposit, credit, insurance product, investment product, claim, entitlement, equity, revenue share, governance right, or tradable asset by implication. Financial meaning requires separate lawful instrument, competent legal review, and regulated-perimeter controls outside DDPGF default posture.

## 21.7 Quantum-Relevant Systems, Semiconductors, Advanced Manufacturing, and Supply Chains

### 21.7.1 Quantum-relevant risk records.

Quantum-relevant risk records shall document risks, dependencies, transition needs, cryptographic exposure, post-quantum migration questions, simulation needs, sensing implications, security implications, standards-interface questions, national capability implications, supply-chain dependencies, and lawful handoff context related to quantum-relevant systems. Records shall identify scope, uncertainty, evidence basis, public-safe status, security sensitivity, dual-use sensitivity, and correction pathway.

Quantum-relevant risk records shall not create security certification, standards authority, public authority approval, procurement readiness, national security determination, financeability, or deployment authorization.

### 21.7.2 Post-quantum transition objects.

Post-quantum transition objects shall include inventories, readiness notes, migration guides, cryptographic dependency maps, standards-interface records, learning objects, public-safe summaries, software dependency notes, API notes, and handoff dependency records related to post-quantum security transition. Such objects shall identify scope, affected systems, assumptions, standards-interface references, implementation dependencies, public authority dependencies, support status, and correction pathway.

Post-quantum transition objects shall not certify compliance, approve cryptographic implementations, authorize deployment, or replace competent security and legal review.

### 21.7.3 Semiconductor supply-chain objects.

Semiconductor supply-chain objects shall document chips, components, dependencies, manufacturing constraints, supplier dependencies, export-control sensitivity, advanced manufacturing dependencies, critical infrastructure relevance, cyber-physical relevance, national capability context, public authority dependencies, and handoff dependencies. Records shall classify sensitivity, access, public-safe status, dual-use concerns, provider-neutrality notes, and correction pathways.

Such objects shall not create procurement recommendation, supplier approval, export authorization, public authority approval, financeability, insurability, or execution authority.

### 21.7.4 Advanced manufacturing readiness notes.

Advanced manufacturing readiness notes shall document manufacturing methods, production dependencies, quality requirements, safety constraints, digital thread dependencies, robotics dependencies, additive manufacturing objects, industrial data dependencies, workforce dependencies, supply-chain dependencies, standards-interface questions, and lawful handoff conditions. Notes shall identify evidence, assumptions, limitations, support status, and correction pathway.

Readiness notes shall not authorize production, certify manufacturing quality, approve suppliers, create procurement status, or authorize deployment by default.

### 21.7.5 Export-control and dual-use sensitivity.

Export-control and dual-use sensitivity records shall identify whether objects may implicate export controls, sanctions, dual-use concerns, national security sensitivities, cyber risks, biosecurity risks, advanced manufacturing controls, semiconductor restrictions, quantum-relevant restrictions, aerospace or drone restrictions, or other controlled-technology concerns. These records shall trigger legal-boundary review, access restriction, public-safe transformation, secure-room handling, handoff controls, and archive rules.

Such records shall not be legal determinations, export licenses, sanctions advice, public authority approvals, or authorization to transfer controlled technology. Competent legal review is required for reliance.

### 21.7.6 Standards-interface records.

Standards-interface records shall document relevant standards, specifications, protocols, testing profiles, interoperability profiles, conformance questions, gaps, and boundary conditions for quantum-relevant systems, semiconductors, advanced manufacturing, supply chains, security, digital trust, and industrial systems. Records shall identify standards references, mapping status, uncertainty, review status, and correction pathway.

Standards-interface records shall not create standards authority, conformance certification, legal standard adoption, public authority approval, procurement qualification, or deployment authorization.

### 21.7.7 National capability records.

National capability records shall document national capacity, skills, infrastructure, supply-chain assets, research capacity, workforce needs, data needs, compute needs, standards needs, security needs, and handoff dependencies in quantum-relevant, semiconductor, advanced manufacturing, and supply-chain contexts. Records shall support National Portfolios, Academy pathways, Foundry builds, Reports, Nexus Universe preparation, and lawful handoff context.

National capability records shall not be country rankings, national security determinations, industrial policy decisions, procurement decisions, public finance allocations, or execution authority.

### 21.7.8 Handoff dependency records.

Handoff dependency records shall identify technical, legal, export-control, dual-use, standards, security, supplier, workforce, data, infrastructure, finance-readiness, insurance-readiness, procurement, and public authority dependencies for quantum-relevant systems, semiconductors, advanced manufacturing, and supply-chain objects. Records shall include recipient responsibilities, correction pathways, and recall mechanisms.

Handoff dependency records shall not authorize export, procurement, manufacturing, deployment, financing, insurance, public authority action, or execution.

## 21.8 Biosecurity-Sensitive and Health-Relevant Innovation

### 21.8.1 Biosecurity-sensitive intake.

Biosecurity-sensitive intake shall apply to objects involving biological systems, biosecurity, biosafety, health data, pathogen-adjacent information, dual-use life science concerns, public health systems, One Health workflows, environmental health, food system biosecurity, lab methods, surveillance-sensitive health data, sensitive species-health intersections, or health-relevant AI and data workflows. Intake shall record object class, sensitivity, public-safe status, data-use label, AI-use label, dual-use concerns, health data controls, public authority dependencies, protected knowledge controls, review needs, access class, incident pathway, and correction pathway.

Biosecurity-sensitive objects shall be restricted where publication, AI use, data release, or operationalization could create harm. Intake shall not create medical approval, public health decision, biosafety certification, regulatory approval, or execution authority.

### 21.8.2 Health data controls.

Health data controls shall apply to health-sensitive data, public health data, climate-health data, One Health data, WFEH-B health data, community health data, youth health data, health-related learning records, health-related survey data, and health-relevant AI workflows. Controls shall include data minimization, purpose limitation, access control, consent or permission where required, de-identification or aggregation where appropriate, AI-use limits, cross-border controls, public-safe transformation, data-room or compute-to-data workflows, and correction pathways.

Health data controls shall not create medical decision authority, public health approval, insurance approval, employment decision authority, financeability, or deployment authorization.

### 21.8.3 Public health boundary records.

Public health boundary records shall distinguish DDPGF health-relevant learning, research, public-safe reporting, data governance, Observatory signals, DRI indicators, Studio scenarios, and National Portfolio context from public health authority decisions. Records shall identify public authority dependencies, no-medical-advice notices, no-public-health-decision notices, no-warning notices, public-safe communication conditions, and escalation pathways where appropriate.

Public health boundary records shall prevent DDPGF outputs from being mistaken for medical advice, diagnosis, treatment guidance, public health orders, public warnings, or emergency instructions.

### 21.8.4 Dual-use sensitivity.

Dual-use sensitivity shall be recorded where biological, health, environmental, AI, cyber, data, geospatial, manufacturing, or lab-related objects could be misused to cause harm. Records shall identify sensitivity basis, access restrictions, public-safe transformation, secure-room requirements, publication limits, AI-use limits, review needs, incident response, and archive rules.

Dual-use sensitivity records shall not be public authority determinations, legal advice, regulatory approvals, or authorization to handle controlled materials. They are internal governance and routing controls.

### 21.8.5 Protected knowledge controls.

Protected knowledge controls shall apply to community health knowledge, Indigenous health knowledge where applicable, ecological-health knowledge, sensitive species-health data, traditional knowledge, local practices, sacred or culturally sensitive health-related knowledge, and other protected knowledge connected to biosecurity or health innovation. Controls may restrict attribution, publication, translation, AI training, data release, handoff, and archive access.

Protected knowledge shall not be extracted or operationalized under the banner of health innovation without lawful, protocol-compliant, and consent-aware authorization.

### 21.8.6 Public-safe publication.

Public-safe publication for biosecurity-sensitive and health-relevant objects shall remove or restrict details that could enable misuse, disclose private health information, expose protected knowledge, stigmatize communities, create false certainty, trigger panic, imply medical advice, imply public health authority, or create operational instructions. Public-safe publications shall include scope, limitations, uncertainty, confidence where applicable, no-medical-advice notices, no-public-health-decision notices, no-warning notices, correction pathways, and archive rules.

Publication shall not create medical approval, public health approval, clinical validation, regulatory approval, public warning, or deployment authorization.

### 21.8.7 Public authority learning boundary.

Public authority learning boundary records shall distinguish health-relevant public authority learning from official public health action. Public authorities may observe, learn, discuss, review public-safe summaries, participate in Studio workflows, or examine readiness context without creating official policy, public health order, regulatory decision, medical guidance, public warning, procurement, public finance allocation, or emergency command by implication.

Official public health action remains outside DDPGF and must be taken by competent authorities through lawful processes.

### 21.8.8 No medical or public health decision by implication.

No biosecurity-sensitive object, health-relevant object, health data object, public-safe health summary, One Health object, climate-health object, WFEH-B health object, Studio workflow, DRI indicator, Observatory signal, dashboard, Report, Marketplace listing, Registry record, Grid input, TRL note, Nexus Universe presentation, Academy object, Campaign object, or handoff context shall be treated as medical advice, diagnosis, treatment guidance, clinical decision, public health order, public warning, emergency instruction, regulatory approval, procurement approval, finance approval, insurance approval, deployment authorization, or execution authority by implication.


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