# llm_cache

draft_name: iso-iec-awi-ts-25569
prompt_hash: 50455a2def6545a2
model: claude-haiku-4-5-20251001
request_json: batch[0]
created_at: 2026-03-08T17:17:59.624497+00:00
response_json:
  {"name": "iso-iec-awi-ts-25569", "s": "Offers practical implementation guidance on de-identifying data for machine learning model development. Covers methods applicable throughout ML pipeline. Bridges privacy requirements with ML engineering practices.", "n": 3, "nn": "De-identification in ML context is moderately novel; combines established privacy techniques with ML-specific considerations", "m": 3, "mn": "Mid-stage development; practical methods exist but standardized implementation guidance is still emerging", "o": 3, "on": "Moderate overlap with data privacy standards (GDPR, etc.); adds ML-specific perspective", "mo": 3, "mon": "Steady momentum driven by privacy regulation compliance needs", "r": 4, "rn": "High relevance for organizations managing sensitive data in ML; addresses regulatory requirements", "c": ["Policy/governance", "Data formats/interop"]}


draft_name: iso-iec-awi-ts-25570
prompt_hash: b97d302189bd553f
model: claude-haiku-4-5-20251001
request_json: batch[0]
created_at: 2026-03-08T17:17:59.634401+00:00
response_json:
  {"name": "iso-iec-awi-ts-25570", "s": "Provides statistical methods and metrics for assessing AI system reliability. Includes procedures for reliability evaluation from quantitative perspective. Enables systematic reliability measurement.", "n": 3, "nn": "AI reliability metrics are moderately novel; applies classical reliability engineering to AI domain with new considerations", "m": 2, "mn": "Early AWI stage; statistical approaches exist but AI-specific reliability frameworks still developing", "o": 2, "on": "Relatively unique in AI context; builds on classical reliability engineering with limited direct overlap", "mo": 3, "mon": "Moderate momentum as reliability becomes critical concern for AI deployment", "r": 4, "rn": "High relevance for mission-critical AI deployments; essential for assurance and compliance", "c": ["AI safety/alignment", "Policy/governance"]}


draft_name: iso-iec-awi-ts-25571
prompt_hash: 28b90bd9198709ae
model: claude-haiku-4-5-20251001
request_json: batch[0]
created_at: 2026-03-08T17:17:59.644460+00:00
response_json:
  {"name": "iso-iec-awi-ts-25571", "s": "Provides template and instructions for documenting ethical issues across AI system lifecycle stages. Practical tool for stakeholders to identify and record ethical considerations. Facilitates systematic ethical governance.", "n": 2, "nn": "Ethical documentation templates are incremental; applies existing ethics frameworks to structured documentation format", "m": 2, "mn": "Early AWI stage; template-based approach is practical but lacks empirical validation across diverse contexts", "o": 3, "on": "Moderate overlap with broader AI ethics frameworks and governance documentation practices", "mo": 2, "mon": "Lower momentum; ethical documentation valuable but less urgent than safety/risk guidance", "r": 3, "rn": "Relevant for organizations formalizing ethics processes; moderately important for governance", "c": ["Policy/governance", "Human-agent interaction"]}


draft_name: iso-iec-cd-tr-18988
prompt_hash: d8e7be0c7d991c9d
model: claude-haiku-4-5-20251001
request_json: batch[0]
created_at: 2026-03-08T17:20:47.339344+00:00
response_json:
  {"name": "iso-iec-cd-tr-18988", "s": "Technical report on applying AI technologies within health informatics systems. Covers standards for AI deployment in healthcare contexts.", "n": 3, "nn": "Health-specific AI application guidance; moderate novelty as domain-focused variant of broader AI standards", "m": 3, "mn": "Committee draft stage indicates moderate maturity; health informatics adds specificity but builds on established AI frameworks", "o": 2, "on": "Limited overlap with other drafts; domain-specific rather than foundational infrastructure focus", "mo": 2, "mon": "Early stage (CD) with healthcare focus may limit broader adoption momentum outside medical sector", "r": 3, "rn": "Relevant for healthcare AI deployment but niche applicability compared to general AI infrastructure standards", "c": ["Policy/governance", "Human-agent interaction", "AI safety/alignment"]}


draft_name: iso-awi-tr-3510
prompt_hash: 42a9014a1b5a21af
model: claude-haiku-4-5-20251001
request_json: batch[0]
created_at: 2026-03-08T17:20:47.359643+00:00
response_json:
  {"name": "iso-awi-tr-3510", "s": "Standard for communication interfaces enabling interoperability between supervisor systems and heterogeneous autonomous controllers in mining. Addresses A2A protocol requirements.", "n": 3, "nn": "Mining-specific autonomous coordination; novel application domain but follows established interoperability patterns", "m": 2, "mn": "Early stage (AWI) with domain-specific focus; technical maturity depends on mining automation precedents", "o": 1, "on": "Highly unique to mining sector; minimal overlap with other AI/agent infrastructure standards", "mo": 2, "mon": "Niche industrial application may limit cross-sector momentum and adoption velocity", "r": 2, "rn": "Relevant for autonomous mining but limited applicability to broader AI agent ecosystems", "c": ["A2A protocols", "Autonomous netops", "Data formats/interop"]}


draft_name: iso-iec-awi-25589
prompt_hash: 7fc54162f698fe7c
model: claude-haiku-4-5-20251001
request_json: batch[0]
created_at: 2026-03-08T17:20:47.369356+00:00
response_json:
  {"name": "iso-iec-awi-25589", "s": "Framework defining concepts, design principles, and technical characteristics for human-machine teaming. Provides guidance for integrating human interaction with machine intelligence.", "n": 4, "nn": "Strong novelty in systematizing human-machine collaboration; advances beyond traditional human-in-loop patterns with formal framework", "m": 3, "mn": "AWI stage with comprehensive scope; framework provides conceptual maturity though implementation guidance may vary", "o": 2, "on": "Moderate overlap with 25590 on AI output quality; complements but distinct from model-focused standards", "mo": 4, "mon": "High momentum as human-AI teaming is active research area; broad applicability across sectors increases adoption potential", "r": 4, "rn": "Highly relevant; human-machine teaming is critical for responsible AI deployment across industries", "c": ["Human-agent interaction", "AI safety/alignment", "Policy/governance"]}


draft_name: iso-iec-awi-25590
prompt_hash: 570974ca560936fb
model: claude-haiku-4-5-20251001
request_json: batch[0]
created_at: 2026-03-08T17:20:47.379970+00:00
response_json:
  {"name": "iso-iec-awi-25590", "s": "Provides guidance for measuring and assessing output data quality from generative AI applications. Addresses quality considerations from creation, use, and impact perspectives.", "n": 4, "nn": "Novel focus on generative AI output quality assessment; timely response to emerging generative AI deployment challenges", "m": 3, "mn": "AWI stage indicates framework development underway; quality metrics for generative systems still maturing industry-wide", "o": 2, "on": "Complements 25589 on human-machine interaction; overlaps with model description (25623) but distinct quality focus", "mo": 5, "mon": "Highest momentum; generative AI output quality is urgent concern; strong stakeholder interest across sectors", "r": 5, "rn": "Extremely relevant; output quality directly impacts safety, trustworthiness, and responsible AI deployment", "c": ["AI safety/alignment", "Model serving/inference", "Policy/governance"]}


draft_name: iso-iec-awi-25623
prompt_hash: 88d6ea196a6465db
model: claude-haiku-4-5-20251001
request_json: batch[0]
created_at: 2026-03-08T17:20:47.389282+00:00
response_json:
  {"name": "iso-iec-awi-25623", "s": "Establishes a description framework and documentation elements for ML models across their lifecycle. Supports model management and usage across organizations.", "n": 3, "nn": "Moderate novelty; ML model documentation frameworks exist but standardization adds value for lifecycle management", "m": 3, "mn": "AWI stage; framework concept mature but comprehensive adoption and standardized implementation still developing", "o": 2, "on": "Minimal overlap; model description complements but distinct from quality guidance (25590) and human-machine teaming (25589)", "mo": 3, "mon": "Steady momentum; model documentation is recognized need but lacks urgency of generative AI quality concerns", "r": 4, "rn": "Relevant for organizations managing ML lifecycle; foundational for interoperability and governance", "c": ["Data formats/interop", "Model serving/inference", "Policy/governance"]}


draft_name: iso-iec-awi-42003
prompt_hash: 0a79b9767f6da6b2
model: claude-haiku-4-5-20251001
request_json: batch[0]
created_at: 2026-03-08T17:20:57.084527+00:00
response_json:
  {"name": "iso-iec-awi-42003", "s": "Provides implementation guidance for ISO/IEC 42001 AI management systems standard. Addresses competencies needed for AIMS professionals across organizations of all types and sizes.", "n": 3, "nn": "Implementation guidance is derivative of existing 42001 standard; limited novel technical content", "m": 4, "mn": "Well-established foundational work building on mature 42001 framework", "o": 3, "on": "Moderate overlap with other governance and management system standards in ISO/IEC AI portfolio", "mo": 4, "mon": "Active development with clear practical applications for organizations adopting AI systems", "r": 4, "rn": "High relevance for organizations implementing formal AI governance and management practices", "c": ["Policy/governance", "Human-agent interaction"]}


draft_name: iso-dis-42501
prompt_hash: 8967815075dbb639
model: claude-haiku-4-5-20251001
request_json: batch[0]
created_at: 2026-03-08T17:20:57.105903+00:00
response_json:
  {"name": "iso-dis-42501", "s": "Establishes trustworthiness and safety requirements for digital platform operators in the sharing economy. Applies to platform governance rather than underlying system architecture.", "n": 3, "nn": "Applies established safety/trustworthiness concepts to sharing economy context; moderate novelty", "m": 3, "mn": "Fairly mature domain-specific application though sharing economy platforms continue evolving", "o": 2, "on": "Limited overlap; specialized focus on sharing economy platforms distinguishes from broader AI safety standards", "mo": 3, "mon": "Steady progress but narrower scope than broader AI safety initiatives", "r": 3, "rn": "Relevant for sharing economy operators and platform governance but narrower audience than general AI standards", "c": ["Policy/governance", "AI safety/alignment"]}


draft_name: iso-iec-awi-ts-42119-7
prompt_hash: 13547fcb42f5b234
model: claude-haiku-4-5-20251001
request_json: batch[0]
created_at: 2026-03-08T17:20:57.115721+00:00
response_json:
  {"name": "iso-iec-awi-ts-42119-7", "s": "Provides technology-agnostic guidance for conducting red teaming assessments on AI systems. Covers risk identification, methodologies, documentation, and lifecycle integration aligned with ISO/IEC 29119.", "n": 4, "nn": "Red teaming for AI is emerging area with novel assessment methodologies not fully standardized elsewhere", "m": 2, "mn": "Early-stage technical guidance; red teaming practices for AI still evolving and maturing", "o": 2, "on": "Minimal overlap; red teaming guidance is distinct from other AI testing and safety standards", "mo": 5, "mon": "Strong momentum driven by heightened focus on AI robustness, adversarial testing, and safety validation", "r": 4, "rn": "Highly relevant as organizations increasingly prioritize adversarial testing and AI system robustness", "c": ["AI safety/alignment"]}


draft_name: iso-iec-awi-25704
prompt_hash: 4d285fa121ef8616
model: claude-haiku-4-5-20251001
request_json: batch[0]
created_at: 2026-03-08T17:20:57.124007+00:00
response_json:
  {"name": "iso-iec-awi-25704", "s": "Defines process assessment model for evaluating AI system lifecycle process capability. Provides measurement framework and indicators for determining organizational AI process maturity levels.", "n": 3, "nn": "Applies established process assessment methodology (ISO/IEC 33020) to AI domain; incremental rather than novel", "m": 3, "mn": "Builds on mature process assessment frameworks but AI lifecycle application still developing", "o": 4, "on": "Significant overlap with ISO/IEC 42001 governance frameworks and other AI lifecycle standards", "mo": 3, "mon": "Moderate progress; process assessment maturity models gaining traction in AI governance", "r": 3, "rn": "Relevant for organizations seeking to measure and improve AI development and deployment processes", "c": ["Policy/governance"]}


draft_name: iso-cd-21520
prompt_hash: 66bf47a19e1e3bcf
model: claude-haiku-4-5-20251001
request_json: batch[0]
created_at: 2026-03-08T17:20:57.132138+00:00
response_json:
  {"name": "iso-cd-21520", "s": "Outlines AI concepts, applications, and implications for project, programme, and portfolio management. Provides governance framework and guidance on security, privacy, bias, and ethics for P3M organizations.", "n": 2, "nn": "Applies AI to established P3M discipline; limited novel AI technical content, mostly domain adaptation", "m": 3, "mn": "Mature P3M practices with emerging AI integration; foundational content still being standardized", "o": 4, "on": "Moderate-high overlap with governance, policy, and organizational AI management standards", "mo": 3, "mon": "Steady adoption of AI in P3M contexts but narrower focus than broader AI standards", "r": 3, "rn": "Relevant for P3M practitioners and organizations managing projects with AI components", "c": ["Policy/governance", "Human-agent interaction"]}


draft_name: iec-np-ts-62366-3
prompt_hash: f88a07bd91887095
model: claude-haiku-4-5-20251001
request_json: batch[0]
created_at: 2026-03-08T17:21:09.133006+00:00
response_json:
  {"name": "iec-np-ts-62366-3", "s": "Guidance document for applying usability engineering processes to medical devices using AI/ML technology. Builds on IEC 62366-1 standards with specific considerations for AI-based medical device development.", "n": 3, "nn": "Applies existing usability framework to emerging AI/ML medical device domain; moderate novelty in specialization rather than new concepts", "m": 4, "mn": "Technical specification stage with established parent standard (IEC 62366-1); guidance-focused approach indicates mature thinking", "o": 2, "on": "Unique focus on medical device usability with AI/ML; minimal overlap with other AI governance/testing standards listed", "mo": 2, "mon": "Technical specification status suggests slower progress; niche medical device focus limits broader AI community momentum", "r": 3, "rn": "Relevant for medical device sector specifically; human-device interaction aspects valuable but limited general AI applicability", "c": ["Policy/governance", "Human-agent interaction", "AI safety/alignment"]}


draft_name: iso-iec-awi-ts-42119-8
prompt_hash: 5ae6e1bb0b275218
model: claude-haiku-4-5-20251001
request_json: batch[0]
created_at: 2026-03-08T17:21:09.153642+00:00
response_json:
  {"name": "iso-iec-awi-ts-42119-8", "s": "Quality assessment framework for generative AI text-to-text systems using prompt-based approaches. Covers testing methodologies including red teaming, safety considerations, and results interpretation.", "n": 4, "nn": "Addresses emerging generative AI testing gap; red teaming methodology and prompt-based assessment represent novel standardization areas", "m": 3, "mn": "AWI (Advanced Work Item) stage indicates early development; builds on existing standards (23282, 42119-7) suggesting incremental maturity", "o": 3, "on": "Moderate overlap with AI safety/alignment and testing approaches; complements resilience assessment drafts", "mo": 4, "mon": "Generative AI focus aligns with current technology momentum; prompt engineering and LLM testing are high-priority areas", "r": 5, "rn": "Highly relevant to current AI deployment challenges; directly addresses practical quality assessment for widely-deployed generative systems", "c": ["AI safety/alignment", "Model serving/inference", "Data formats/interop", "Policy/governance"]}


draft_name: iso-iec-awi-25870
prompt_hash: 8b5b828b1e52f08b
model: claude-haiku-4-5-20251001
request_json: batch[0]
created_at: 2026-03-08T17:21:09.162736+00:00
response_json:
  {"name": "iso-iec-awi-25870", "s": "Framework specifying data elements for reporting AI incidents across all organization types. Establishes standardized incident documentation structure.", "n": 3, "nn": "Incident reporting structure is relatively straightforward; value lies in cross-organizational standardization rather than conceptual novelty", "m": 2, "mn": "AWI status; incident reporting frameworks are established in other domains but AI-specific application is nascent", "o": 2, "on": "Unique incident reporting focus; complements but distinct from safety/resilience assessment standards", "mo": 3, "mon": "Growing awareness of AI incident tracking needs; momentum moderate as field still establishing incident reporting culture", "r": 4, "rn": "Increasingly relevant as AI systems proliferate; incident data collection critical for safety and governance across sectors", "c": ["Policy/governance", "AI safety/alignment", "Data formats/interop"]}


draft_name: iso-iec-awi-25872-1
prompt_hash: fa62fccf36861915
model: claude-haiku-4-5-20251001
request_json: batch[0]
created_at: 2026-03-08T17:21:09.173623+00:00
response_json:
  {"name": "iso-iec-awi-25872-1", "s": "Framework and guidance for knowledge enhancement methods applied to pretrained machine learning models. Part 1 establishes conceptual foundations and enhancement methodologies.", "n": 3, "nn": "Knowledge enhancement is emerging field; framework development moderate novelty as techniques exist but standardization is new", "m": 2, "mn": "Early AWI stage with multi-part structure; foundational framework indicates work still in formulation phase", "o": 1, "on": "Unique focus on knowledge enhancement for pretrained models; minimal overlap with other listed standards", "mo": 2, "mon": "Specialized technical area with limited mainstream momentum; applicable primarily to ML practitioners seeking model improvement", "r": 3, "rn": "Moderately relevant for ML development lifecycle; practical applicability concentrated in model optimization domain", "c": ["Model serving/inference", "Data formats/interop"]}


draft_name: iso-iec-awi-ts-25864
prompt_hash: 61c481389eedf632
model: claude-haiku-4-5-20251001
request_json: batch[0]
created_at: 2026-03-08T17:21:09.185449+00:00
response_json:
  {"name": "iso-iec-awi-ts-25864", "s": "Comprehensive framework providing methods, mechanisms, guidelines, and quantifiable metrics for assessing AI system resilience. Addresses robustness and failure modes.", "n": 4, "nn": "Resilience assessment for AI systems represents emerging standardization need; quantifiable metrics approach is novel contribution", "m": 3, "mn": "AWI TS (Technical Specification) stage indicates developed thinking; resilience frameworks exist in traditional systems but AI-specific adaptation is intermediate", "o": 3, "on": "Moderate overlap with AI safety/alignment and testing standards; complementary to incident reporting and quality assessment drafts", "mo": 4, "mon": "AI resilience and robustness are high-priority current topics; momentum strong as deployments increase demand for reliability guarantees", "r": 5, "rn": "Highly relevant across sectors; resilience metrics essential for critical infrastructure and safety-sensitive AI applications", "c": ["AI safety/alignment", "Policy/governance", "Model serving/inference"]}


draft_name: iso-iec-awi-tr-24030
prompt_hash: 6665ea8a7e88783c
model: claude-haiku-4-5-20251001
request_json: batch[0]
created_at: 2026-03-08T17:21:20.591884+00:00
response_json:
  {"name": "iso-iec-awi-tr-24030", "s": "Compiles representative use cases of AI applications across various domains to inform standardization efforts. Provides practical examples of AI deployment scenarios without prescriptive requirements.", "n": 2, "nn": "Use case collection is foundational but not technically novel; primarily serves as reference material for standards development", "m": 2, "mn": "Early stage technical report (TR) with abstract content; lacks detailed implementation guidance or maturity examples", "o": 2, "on": "Minimal overlap with other drafts; serves as complementary reference material rather than addressing specific technical standards", "mo": 3, "mon": "Moderate momentum as foundational input to AI standardization; useful for scoping but not directly advancing technical specifications", "r": 4, "rn": "Highly relevant for understanding AI application landscape and identifying standardization needs across domains", "c": ["Other AI/agent", "Policy/governance"]}


draft_name: iso-iec-np-25880
prompt_hash: f9e91d83deb65f7a
model: claude-haiku-4-5-20251001
request_json: batch[0]
created_at: 2026-03-08T17:21:20.613336+00:00
response_json:
  {"name": "iso-iec-np-25880", "s": "Specifies organizational requirements and guidance for implementing human-machine teaming in AI systems during practical operation. Addresses how human operators and AI systems collaborate within organizations.", "n": 3, "nn": "Focuses on organizational and procedural aspects of HMT; novel in formalizing team dynamics but builds on established human-factors principles", "m": 3, "mn": "New proposal stage (NP) with clear scope but limited detail; requires substantive development before full standardization", "o": 3, "on": "Moderate overlap with human-agent interaction concerns; some alignment with ISO/AWI 25922 on human-machine allocation", "mo": 4, "mon": "Growing momentum as AI systems increasingly require human oversight; directly addresses practical implementation gaps", "r": 5, "rn": "Critical relevance for safe and effective AI deployment where human judgment remains essential", "c": ["Human-agent interaction", "Policy/governance", "AI safety/alignment"]}


draft_name: iso-awi-tr-25896
prompt_hash: c9f6380d7efd5622
model: claude-haiku-4-5-20251001
request_json: batch[0]
created_at: 2026-03-08T17:21:20.623893+00:00
response_json:
  {"name": "iso-awi-tr-25896", "s": "Addresses application of AI techniques to terminology resource management and standardization. Combines terminology management standards with AI-enabled tools for language and concept handling.", "n": 2, "nn": "Applies existing AI concepts to specialized domain (terminology); incremental rather than fundamentally novel", "m": 2, "mn": "Early working draft stage; narrow applicability to specialized ISO/TC 37 community limits mainstream relevance", "o": 1, "on": "Highly unique focus on terminology management; minimal overlap with other AI standardization efforts", "mo": 2, "mon": "Limited momentum; narrow domain relevance and specialized audience reduces driving force for progression", "r": 2, "rn": "Low-to-moderate relevance outside terminology standardization community; niche application area", "c": ["Data formats/interop", "Other AI/agent"]}


draft_name: iso-iec-awi-25058
prompt_hash: 9f25407b3fdccf62
model: claude-haiku-4-5-20251001
request_json: batch[0]
created_at: 2026-03-08T17:21:20.633689+00:00
response_json:
  {"name": "iso-iec-awi-25058", "s": "Provides measurement methods and quality evaluation guidance for AI systems using quality models defined in ISO/IEC 25059. Establishes metrics and processes for assessing AI system quality across organizations.", "n": 4, "nn": "Novel in formalizing quality measurement for AI systems; extends traditional SQuaRE framework to AI-specific concerns", "m": 3, "mn": "Working draft stage with reference to companion standard (25059); substantive but requires refinement of measurement approaches", "o": 2, "on": "Low overlap; specialized quality measurement focus distinct from other standards, though somewhat related to safety/alignment frameworks", "mo": 4, "mon": "Strong momentum as organizations need standardized AI quality assessment; addresses critical gap in evaluation practices", "r": 5, "rn": "Highly relevant for organizations developing and deploying AI systems; essential for quality assurance and governance", "c": ["Policy/governance", "AI safety/alignment", "Other AI/agent"]}


draft_name: iso-awi-25922
prompt_hash: 5b55650b97878c8c
model: claude-haiku-4-5-20251001
request_json: batch[0]
created_at: 2026-03-08T17:21:20.681329+00:00
response_json:
  {"name": "iso-awi-25922", "s": "Specifies methods for defining and allocating capabilities and responsibilities between maritime autonomous surface ships (MASS) and remote human operators. Establishes standardized approaches for human-machine task allocation in maritime automation.", "n": 4, "nn": "Novel in formalizing MASS operator interaction; applies human-machine teaming principles to critical maritime domain with safety implications", "m": 3, "mn": "Working draft with clear scope and practical maritime context; moderately mature but requires validation through maritime industry input", "o": 4, "on": "Significant overlap with ISO/IEC NP 25880 on human-machine teaming principles; both address capability/responsibility allocation", "mo": 3, "mon": "Moderate momentum driven by maritime industry autonomous systems development; domain-specific rather than broadly transformative", "r": 4, "rn": "Highly relevant for maritime industry and autonomous systems safety; moderate broader relevance for human-agent interaction principles", "c": ["Human-agent interaction", "Autonomous netops", "Policy/governance", "AI safety/alignment"]}


draft_name: iso-awi-25934
prompt_hash: 7cb8ff372faf4a58
model: claude-haiku-4-5-20251001
request_json: batch[0]
created_at: 2026-03-08T17:21:31.428154+00:00
response_json:
  {"name": "iso-awi-25934", "s": "Establishes structured verification procedures for autonomous navigation systems in Maritime Autonomous Surface Ships (MASS). Addresses safety and reliability assessment across different operational modes, supporting manufacturers, operators, and regulators in developing MASS-specific verification frameworks.", "n": 3, "nn": "Verification procedures for maritime autonomous systems are specialized; moderate novelty in applying ISO safety standards to MASS domain", "m": 2, "mn": "Early stage (AWI=Active Work Item); framework still under development with limited implementation guidance", "o": 3, "on": "Overlaps with general autonomous system verification standards but maritime-specific niche reduces overlap", "mo": 2, "mon": "Niche maritime focus limits broad momentum; relevance primarily to shipping industry stakeholders", "r": 3, "rn": "Relevant to autonomous maritime operations but limited to specific vertical; emerging regulatory importance", "c": ["Autonomous netops", "Policy/governance", "Data formats/interop"]}


draft_name: iso-iec-cd-30147
prompt_hash: ace8b935b410ae6e
model: claude-haiku-4-5-20251001
request_json: batch[0]
created_at: 2026-03-08T17:21:31.448289+00:00
response_json:
  {"name": "iso-iec-cd-30147", "s": "Integrates IoT trustworthiness verification activities into ISO/IEC/IEEE 15288 system engineering processes. Bridges IoT-specific security and reliability concerns with established systems engineering methodologies.", "n": 3, "nn": "Integration of trustworthiness into established SE processes shows moderate novelty; trustworthiness frameworks exist but process integration is somewhat novel", "m": 3, "mn": "Committee Draft stage indicates substantive development; closer to standardization than early-stage work", "o": 4, "on": "Significant overlap with existing ISO/IEC/IEEE 15288 and other IoT security standards; incremental refinement", "mo": 3, "mon": "IoT trustworthiness is active area; moderate momentum but integration with 15288 is specialized", "r": 4, "rn": "High relevance to IoT system engineering; applicable across IoT verticals", "c": ["Data formats/interop", "AI safety/alignment", "Policy/governance"]}


draft_name: iso-awi-tr-19182
prompt_hash: ebdb6452038eaf74
model: claude-haiku-4-5-20251001
request_json: batch[0]
created_at: 2026-03-08T17:21:31.456403+00:00
response_json:
  {"name": "iso-awi-tr-19182", "s": "Provides guidance on incorporating Geospatial Artificial Intelligence (GeoAI) into geographic information standards. Addresses integration of AI techniques within GIS and geospatial data frameworks.", "n": 3, "nn": "GeoAI is emerging field; guidance on standards integration is moderately novel but builds on existing AI and GIS standards", "m": 2, "mn": "Technical Report (TR) in AWI stage; exploratory guidance rather than normative standard", "o": 3, "on": "Bridges GIS and AI domains with some overlap to broader AI standards and geospatial data standards", "mo": 2, "mon": "GeoAI gaining attention but geographic information standards community is relatively specialized", "r": 3, "rn": "Relevant to GIS practitioners and location-based AI applications; moderate general relevance", "c": ["Data formats/interop", "Model serving/inference", "Other AI/agent"]}


draft_name: iso-iec-awi-23888-5
prompt_hash: ffc7777018d74884
model: claude-haiku-4-5-20251001
request_json: batch[0]
created_at: 2026-03-08T17:21:31.465122+00:00
response_json:
  {"name": "iso-iec-awi-23888-5", "s": "Defines bitstream syntax, semantics, and decoding procedures for AI-based dynamic point cloud compression. Specifies encoding format and reconstruction methods for efficient point cloud representation.", "n": 4, "nn": "Dynamic point cloud compression with AI is novel; combines emerging compression techniques with point cloud standards", "m": 2, "mn": "Early AWI stage with minimal abstract detail; technical specifications still under development", "o": 2, "on": "Relatively unique focus on AI-based dynamic compression; limited overlap with existing standards", "mo": 3, "mon": "3D/point cloud processing gaining momentum; AI-based compression increasingly important for multimedia", "r": 3, "rn": "Relevant to 3D imaging, robotics, autonomous systems; emerging application domains", "c": ["Data formats/interop", "Model serving/inference", "Other AI/agent"]}


draft_name: iso-iec-awi-23888-4
prompt_hash: 302a9a91814af38d
model: claude-haiku-4-5-20251001
request_json: batch[0]
created_at: 2026-03-08T17:21:31.473814+00:00
response_json:
  {"name": "iso-iec-awi-23888-4", "s": "Defines bitstream format and decoding process for compression of machine-extracted video features. Optimizes feature coding for efficiency in bitrate, machine task performance, and computational complexity.", "n": 4, "nn": "Feature coding for machines (vs. humans) is relatively novel; focuses on ML-optimized compression rather than human perception", "m": 2, "mn": "Early AWI stage with limited specification detail; framework and requirements still being formulated", "o": 2, "on": "Unique focus on machine-centric feature compression; minimal overlap with human-centric video standards", "mo": 3, "mon": "ML inference efficiency and edge computing drive momentum; feature compression increasingly relevant", "r": 4, "rn": "Highly relevant to video analytics, edge ML, autonomous systems requiring video understanding", "c": ["Data formats/interop", "Model serving/inference", "ML traffic mgmt"]}


draft_name: iso-iec-awi-26090-1
prompt_hash: 2eef7cbe5cbebcd0
model: claude-haiku-4-5-20251001
request_json: batch[0]
created_at: 2026-03-08T17:21:42.579070+00:00
response_json:
  {"name": "iso-iec-awi-26090-1", "s": "Establishes framework for evaluating trustworthiness of city knowledge systems in smart city contexts. Covers concepts, characteristics, methodology, and use cases for knowledge trustworthiness assessment.", "n": 3, "nn": "Applies trustworthiness evaluation specifically to city/urban knowledge domains; moderately novel framing for smart cities context", "m": 2, "mn": "Early stage AWI (Advanced Work Item) with foundational overview content; lacks detailed technical specifications", "o": 3, "on": "Moderate overlap with general AI evaluation frameworks; distinct urban/city knowledge focus reduces overlap", "mo": 2, "mon": "Early development stage limits momentum indicators; city knowledge domain is emerging area", "r": 3, "rn": "Relevant for smart city deployments and urban data governance; limited relevance to broader AI systems", "c": ["Policy/governance", "AI safety/alignment", "Data formats/interop"]}


draft_name: iso-iec-awi-26160
prompt_hash: ef3db012fdd80d74
model: claude-haiku-4-5-20251001
request_json: batch[0]
created_at: 2026-03-08T17:21:42.599699+00:00
response_json:
  {"name": "iso-iec-awi-26160", "s": "Extends ISO/IEC 15408 (Common Criteria) and ISO/IEC 18045 evaluation methodologies with AI-specific security assurance and functional requirements. Focuses on security evaluation of AI systems.", "n": 4, "nn": "Novel adaptation of mature security evaluation standards to AI context; addresses significant gap in AI security assurance", "m": 3, "mn": "Builds on established CC/CEM frameworks; AIspecific extensions are in development stage", "o": 2, "on": "Low overlap; uniquely bridges security evaluation standards with AI functionality assessment", "mo": 4, "mon": "Addresses critical security evaluation gap; strong momentum from established CC ecosystem", "r": 4, "rn": "Highly relevant for regulated AI deployments; essential for security-critical AI systems evaluation", "c": ["AI safety/alignment", "Policy/governance", "Model serving/inference"]}


draft_name: iso-iec-pwi-26200
prompt_hash: 232a2bd718bb6f0e
model: claude-haiku-4-5-20251001
request_json: batch[0]
created_at: 2026-03-08T17:21:42.608678+00:00
response_json:
  {"name": "iso-iec-pwi-26200", "s": "Proposes comprehensive standard for LLM evaluation, ethical governance, interoperability, testing methodologies, bias mitigation, and secure deployment across domains. Scope limited to open, general-purpose LLMs.", "n": 3, "nn": "Moderately novel; consolidates existing LLM evaluation practices into single standard; interoperability focus adds value", "m": 2, "mn": "Early PWI (Proposal Work Item) stage; LLM field evolving rapidly, standard maturity uncertain", "o": 4, "on": "Significant overlap with emerging LLM evaluation frameworks and guidelines; covers well-trodden ground", "mo": 3, "mon": "LLM standardization is active area; proposal timing good but faces competition from de facto standards", "r": 5, "rn": "Extremely relevant; LLMs are dominant AI deployment model; covers healthcare, finance, education sectors", "c": ["AI safety/alignment", "Policy/governance", "Model serving/inference", "Data formats/interop", "Human-agent interaction"]}


draft_name: iso-np-ts-25280-1
prompt_hash: 6ba5743fa398518f
model: claude-haiku-4-5-20251001
request_json: batch[0]
created_at: 2026-03-08T17:21:42.616759+00:00
response_json:
  {"name": "iso-np-ts-25280-1", "s": "Establishes principles and considerations for records management in AI-enabled environments, covering both AI-generated and AI-associated records across all organization types.", "n": 3, "nn": "Moderately novel application of records management to AI context; fills gap in documentation standards for AI systems", "m": 2, "mn": "Early NP (New Proposal) stage with principles-level guidance; lacks detailed implementation specifications", "o": 2, "on": "Low overlap; unique focus on records management dimension of AI rather than AI evaluation itself", "mo": 2, "mon": "Records management + AI is emerging concern; early development stage limits momentum visibility", "r": 3, "rn": "Moderately relevant for compliance and governance; critical for regulated sectors; less relevant for AI capabilities", "c": ["Policy/governance", "Data formats/interop", "Other AI/agent"]}


draft_name: iso-np-ts-25280-2
prompt_hash: 6a3459e150a7a5c2
model: claude-haiku-4-5-20251001
request_json: batch[0]
created_at: 2026-03-08T17:21:42.624723+00:00
response_json:
  {"name": "iso-np-ts-25280-2", "s": "Outlines principles and considerations for applying AI capabilities to records management functions, controls, processes, and systems across all organization types.", "n": 3, "nn": "Moderately novel; focuses on AI-as-tool for records rather than records-of-AI; complements Part 1", "m": 2, "mn": "Early NP stage with principles-level content; application-specific but not detailed implementation", "o": 2, "on": "Low overlap; distinct from Part 1; narrow focus on records automation reduces overlap with broader AI standards", "mo": 2, "mon": "Early stage; records automation is mature domain, AI application is incremental advancement", "r": 2, "rn": "Limited relevance outside records/information management domain; niche application focus", "c": ["Policy/governance", "Data formats/interop", "Other AI/agent"]}


draft_name: iso-awi-26253
prompt_hash: 29dfaa0288ad92cc
model: claude-haiku-4-5-20251001
request_json: batch[0]
created_at: 2026-03-08T17:22:01.777579+00:00
response_json:
  {"name": "iso-awi-26253", "s": "Guidelines for implementing centralized control systems for autonomous mobile robots managing pharmaceutical logistics in hospitals. Covers safe operation, real-time monitoring, communication, scheduling, and record-keeping for AMR-based internal hospital logistics.", "n": 3, "nn": "Specialized application domain (hospital pharma logistics) with established AMR technology; moderate novelty in standardizing hospital-specific CCS requirements", "m": 4, "mn": "Well-defined problem space with mature robotics foundations; practical operational focus suggests implementation-ready guidance", "o": 2, "on": "Minimal overlap with other AI/agent standards; niche healthcare robotics focus distinct from broader AI governance and NLP work", "mo": 3, "mon": "Healthcare automation is active area; pharmaceutical supply chain efficiency has ongoing industry momentum", "r": 4, "rn": "High relevance for healthcare organizations and medical device manufacturers; addresses critical operational safety and logistics coordination", "c": ["Autonomous netops", "ML traffic mgmt", "Agent identity/auth", "Policy/governance", "Human-agent interaction"]}


draft_name: iso-iec-22989-2022-awi-amd-2
prompt_hash: eb273154dbc98166
model: claude-haiku-4-5-20251001
request_json: batch[0]
created_at: 2026-03-08T17:22:01.799038+00:00
response_json:
  {"name": "iso-iec-22989-2022-awi-amd-2", "s": "Amendment 2 to ISO/IEC 22989:2022 updating AI concepts and terminology standards. Incremental refinement of existing AI vocabulary and definitions framework.", "n": 1, "nn": "Maintenance amendment to established standard; minimal substantive novelty expected in terminology updates", "m": 5, "mn": "Mature foundational standard with established terminology; amendment represents evolution of stable reference material", "o": 4, "on": "Heavy conceptual overlap with companion ISO/IEC 23053 amendment and other AI/ML standards relying on shared terminology", "mo": 2, "mon": "Routine standards maintenance; incremental updates typically lower industry momentum than new frameworks", "r": 4, "rn": "High relevance as foundational reference for AI terminology across all sectors and standards development", "c": ["Data formats/interop", "Policy/governance", "Other AI/agent"]}


draft_name: iso-iec-23053-2022-awi-amd-2
prompt_hash: 71189c7395d07c98
model: claude-haiku-4-5-20251001
request_json: batch[0]
created_at: 2026-03-08T17:22:01.807585+00:00
response_json:
  {"name": "iso-iec-23053-2022-awi-amd-2", "s": "Amendment 2 to ISO/IEC 23053:2022 updating the AI/ML systems framework. Incremental refinement to existing framework for AI systems using machine learning approaches.", "n": 1, "nn": "Maintenance amendment to established ML systems framework; limited novelty in framework evolution", "m": 5, "mn": "Mature framework standard with established guidance; amendment represents incremental maturation", "o": 4, "on": "Significant overlap with companion ISO/IEC 22989 amendment and broad alignment with other ISO/IEC AI standards", "mo": 2, "mon": "Standards maintenance activity; routine updates show stable but not accelerating momentum", "r": 4, "rn": "High relevance as core reference framework for ML systems implementation across organizations and sectors", "c": ["Model serving/inference", "Policy/governance", "Data formats/interop", "Other AI/agent"]}


draft_name: iso-iec-np-ts-26312
prompt_hash: f9e2e32ef0abd52c
model: claude-haiku-4-5-20251001
request_json: batch[0]
created_at: 2026-03-08T17:22:01.816713+00:00
response_json:
  {"name": "iso-iec-np-ts-26312", "s": "Technical specification for healthcare organizations to identify and mitigate bias in AI systems across procurement, implementation, and monitoring phases. Addresses algorithmic, data, labeling, and deployment-related bias affecting patient and clinician-facing applications.", "n": 4, "nn": "Novel focus on healthcare-specific bias detection and mitigation; comprehensive lifecycle perspective on bias sources is relatively fresh for domain standards", "m": 3, "mn": "Emerging practice area with developing methodologies; practical guidance framework is maturing but implementation approaches still evolving", "o": 2, "on": "Unique intersection of AI bias, healthcare governance, and fairness; limited direct overlap with other standards except general AI safety principles", "mo": 5, "mon": "Bias in healthcare AI is high-momentum topic; regulatory pressure and equity concerns driving strong standards development interest", "r": 5, "rn": "Critical relevance for healthcare organizations deploying AI; addresses ethical, legal, and patient safety imperatives", "c": ["AI safety/alignment", "Policy/governance", "Human-agent interaction", "Other AI/agent"]}


draft_name: iso-iec-np-ts-26320.2
prompt_hash: 29044cd022a8003e
model: claude-haiku-4-5-20251001
request_json: batch[0]
created_at: 2026-03-08T17:22:01.825612+00:00
response_json:
  {"name": "iso-iec-np-ts-26320.2", "s": "Establishes principles, frameworks, and methods for developing and maintaining corpora for natural language processing systems. Includes corpus construction requirements, application guidance, and quality evaluation measures applicable across AI system lifecycles.", "n": 3, "nn": "Systematic approach to corpus development governance; standards for NLP data quality are valuable but building on established ML data practices", "m": 3, "mn": "Corpus linguistics and ML data engineering are mature; standardizing corpus practices is advancing implementation maturity", "o": 2, "on": "Focused on NLP-specific data requirements; some overlap with general data governance but distinct from broader AI/ML standards", "mo": 4, "mon": "NLP and large language models drive sustained momentum; data quality for LLMs is active standardization priority", "r": 4, "rn": "High relevance for organizations developing or deploying NLP systems; foundational for reproducible and fair language AI", "c": ["Data formats/interop", "Model serving/inference", "Policy/governance", "Other AI/agent"]}


draft_name: iso-cd-19178-2
prompt_hash: 93f0d9d120410e11
model: claude-haiku-4-5-20251001
request_json: batch[0]
created_at: 2026-03-08T17:22:12.622415+00:00
response_json:
  {"name": "iso-cd-19178-2", "s": "Specifies JSON encoding for training data markup language in AI systems. Part of a multi-part ISO standard defining standardized formats for AI training data representation.", "n": 3, "nn": "Incremental standardization work; JSON encoding is a common approach but application to AI training data markup is moderately novel", "m": 4, "mn": "Committee draft stage indicates mature development; JSON is well-established technology applied to defined domain", "o": 2, "on": "Minimal overlap with other drafts; Part 3 covers XML variant but both are encoding-specific rather than functional", "mo": 3, "mon": "Part of established ISO/TC 211 standards program; committee draft status suggests steady progress", "r": 3, "rn": "Relevant for data interoperability in AI systems; practical value depends on adoption of parent standard", "c": ["Data formats/interop"]}


draft_name: iso-cd-19178-3
prompt_hash: bcca56914f3311b5
model: claude-haiku-4-5-20251001
request_json: batch[0]
created_at: 2026-03-08T17:22:12.644708+00:00
response_json:
  {"name": "iso-cd-19178-3", "s": "Specifies XML encoding for training data markup language in AI systems. Part 3 of ISO/IEC 19178 standard for AI training data representation.", "n": 3, "nn": "Parallel to Part 2; XML encoding is traditional approach with moderate novelty in AI training data context", "m": 4, "mn": "Committee draft stage; XML is mature technology; development appears orderly within standards process", "o": 2, "on": "Functionally identical to Part 2 except for encoding format; minimal unique overlap with other drafts", "mo": 3, "mon": "Part of coordinated multi-part standard series; steady committee-level progress", "r": 3, "rn": "Relevant for standardized AI training data interchange; XML variant provides alternative to JSON encoding", "c": ["Data formats/interop"]}


draft_name: iso-iec-np-26582
prompt_hash: 74b796ae1269aa0b
model: claude-haiku-4-5-20251001
request_json: batch[0]
created_at: 2026-03-08T17:22:12.654658+00:00
response_json:
  {"name": "iso-iec-np-26582", "s": "Defines conformity assessment requirements for functional correctness of AI systems based on ISO/IEC 25059 quality models. Applicable to all AI system types.", "n": 4, "nn": "Novel approach to standardized AI correctness assessment; builds on quality model foundation with systematic conformity framework", "m": 2, "mn": "New proposal stage indicates early development; references existing quality standard but conformity assessment methodology still emerging", "o": 1, "on": "Unique focus on functional correctness assessment; no significant overlap with other drafts in this set", "mo": 2, "mon": "New proposal indicates emerging consensus around need; but limited visibility into development momentum", "r": 4, "rn": "Highly relevant for AI safety and governance; addresses critical need for standardized correctness evaluation across AI systems", "c": ["AI safety/alignment", "Policy/governance"]}


draft_name: iso-np-26604-1
prompt_hash: 8733e570ed46caed
model: claude-haiku-4-5-20251001
request_json: batch[0]
created_at: 2026-03-08T17:22:12.665577+00:00
response_json:
  {"name": "iso-np-26604-1", "s": "Specifies safety requirements and verification methods for autonomous agricultural machinery including soil-working operations. Covers design, construction, and safe use guidance.", "n": 3, "nn": "Applies established machinery safety standards to autonomous agriculture; domain-specific but follows conventional safety framework", "m": 2, "mn": "New proposal stage; addresses practical safety gaps in autonomous farm equipment but methodology builds on existing machine safety standards", "o": 1, "on": "Highly specialized to agricultural autonomous machinery; minimal overlap with other AI/agent standards in this set", "mo": 2, "mon": "Emerging need driven by autonomous agricultural equipment development; early proposal stage", "r": 2, "rn": "Relevant primarily for agricultural sector; limited broader applicability to general AI/agent systems", "c": ["Autonomous netops", "AI safety/alignment", "Policy/governance"]}


draft_name: iso-np-26609
prompt_hash: 2fd4b969e11dd354
model: claude-haiku-4-5-20251001
request_json: batch[0]
created_at: 2026-03-08T17:22:12.675388+00:00
response_json:
  {"name": "iso-np-26609", "s": "Sets requirements for assessing AI and Augmented Intelligence systems used with 2D dental radiographs. Domain-specific medical AI application standard.", "n": 2, "nn": "Narrow specialization to dental radiography AI; assessment framework is incremental application of general AI principles to defined domain", "m": 2, "mn": "New proposal; addresses specific healthcare use case but assessment methodology not substantially novel", "o": 1, "on": "Highly specialized to dental imaging; no overlap with other standards in this collection", "mo": 2, "mon": "Emerging need in medical AI regulation but early proposal stage indicates nascent momentum", "r": 2, "rn": "Relevant for dental/medical sector; limited applicability to broader AI/agent systems and standards", "c": ["AI safety/alignment", "Policy/governance"]}


draft_name: iso-np-pas-8800-1
prompt_hash: e6b7b6a4e5d7d35e
model: claude-haiku-4-5-20251001
request_json: batch[0]
created_at: 2026-03-08T17:22:23.481213+00:00
response_json:
  {"name": "iso-np-pas-8800-1", "s": "ISO standard defining key terminology and concepts for safety and AI in road vehicles. Part 1 of a multi-part standard establishing foundational definitions for the field.", "n": 2, "nn": "Definitions and concepts are prerequisite but not novel contributions; primarily normative clarification.", "m": 2, "mn": "Early-stage standard (NP/PAS status) with abstract only; foundational but incomplete specification.", "o": 3, "on": "Moderate overlap with ISO 26262 and ISO 21448 safety frameworks; aligns with NIST AI RMF conceptual scope.", "mo": 2, "mon": "Emerging standard with limited adoption signals; part of active standards development but early maturity.", "r": 4, "rn": "Highly relevant for road vehicle safety governance and regulatory compliance frameworks.", "c": ["Policy/governance", "AI safety/alignment", "Other AI/agent"]}


draft_name: iso-np-pas-8800-2
prompt_hash: 6b7b674db02f8908
model: claude-haiku-4-5-20251001
request_json: batch[0]
created_at: 2026-03-08T17:22:23.501206+00:00
response_json:
  {"name": "iso-np-pas-8800-2", "s": "ISO standard specifying general safety requirements for AI-equipped electrical/electronic systems in production road vehicles. Addresses systematic errors, random hardware failures, and external AI element interactions.", "n": 3, "nn": "Applies established safety methodologies to AI systems; novel integration of AI-specific failure modes with vehicle E/E safety architecture.", "m": 2, "mn": "Incomplete abstract; still in NP/PAS development phase without finalized technical requirements.", "o": 4, "on": "Significant overlap with NIST AI 100-1 risk management framework and ISO 26262 functional safety principles.", "mo": 2, "mon": "Part of emerging standards initiative; unclear adoption trajectory in automotive industry.", "r": 5, "rn": "Critical for automotive safety assurance and regulatory compliance in AI-enabled vehicles.", "c": ["AI safety/alignment", "Policy/governance", "Model serving/inference"]}


draft_name: nist-ai-100-1
prompt_hash: f4728a8a94c44b1b
model: claude-haiku-4-5-20251001
request_json: batch[0]
created_at: 2026-03-08T17:22:23.509932+00:00
response_json:
  {"name": "nist-ai-100-1", "s": "Comprehensive framework for managing AI system risks across full lifecycle, covering trustworthiness dimensions including safety, security, fairness, transparency, and accountability. Published January 2023.", "n": 4, "nn": "Novel integrated approach combining multiple trustworthiness dimensions; establishes new governance paradigm for AI risk management.", "m": 4, "mn": "Mature, published standard with clear structure and broad industry adoption; established reference framework.", "o": 2, "on": "Foundational framework with minimal overlap; complementary to domain-specific standards rather than duplicative.", "mo": 4, "mon": "Strong momentum with widespread adoption across government and private sectors; cited in policy discussions.", "r": 5, "rn": "Essential reference framework for AI governance, risk management, and policy development across all sectors.", "c": ["Policy/governance", "AI safety/alignment", "Human-agent interaction"]}


draft_name: nist-ai-100-2-e2025
prompt_hash: 1169405cef827815
model: claude-haiku-4-5-20251001
request_json: batch[0]
created_at: 2026-03-08T17:22:23.519388+00:00
response_json:
  {"name": "nist-ai-100-2-e2025", "s": "Taxonomy and terminology for adversarial machine learning attacks and mitigations covering evasion, poisoning, and privacy attacks against predictive and generative AI systems. Published March 2025.", "n": 4, "nn": "Comprehensive adversarial ML taxonomy is novel contribution; particularly detailed for LLM attack vectors.", "m": 5, "mn": "Recent publication (2025) with mature framework; builds on established adversarial ML research with industry validation.", "o": 1, "on": "Highly specialized focus with minimal overlap to other frameworks; unique in adversarial ML depth.", "mo": 5, "mon": "Very recent release indicates active development; strong relevance to current AI security concerns.", "r": 4, "rn": "Highly relevant for AI safety, security, and robustness requirements; critical for LLM risk management.", "c": ["AI safety/alignment", "Data formats/interop", "Model serving/inference"]}


draft_name: nist-ai-100-4
prompt_hash: 1a9d1879ac6559b1
model: claude-haiku-4-5-20251001
request_json: batch[0]
created_at: 2026-03-08T17:22:23.528192+00:00
response_json:
  {"name": "nist-ai-100-4", "s": "Guidance on managing risks from synthetic content generated by AI through authentication, provenance tracking, and content labeling mechanisms. Published July 2024.", "n": 3, "nn": "Addresses emerging synthetic content risks; practical mitigation approaches are timely but build on existing verification techniques.", "m": 4, "mn": "Published guidance document with practical applicability; established mitigation strategies presented in structured format.", "o": 2, "on": "Complementary to NIST AI 100-1; focused on specific synthetic content risks with limited overlap to other frameworks.", "mo": 4, "mon": "Recent (2024) publication addressing urgent policy concern; growing industry interest in synthetic content verification.", "r": 4, "rn": "Increasingly relevant for content authenticity, misinformation prevention, and AI transparency requirements.", "c": ["Policy/governance", "Data formats/interop", "AI safety/alignment"]}


draft_name: nist-ai-600-1
prompt_hash: 5dc27917be196be3
model: claude-haiku-4-5-20251001
request_json: batch[0]
created_at: 2026-03-08T17:22:32.999686+00:00
response_json:
  {"name": "nist-ai-600-1", "s": "Companion resource to NIST AI RMF that specifically addresses risks unique to or exacerbated by generative AI systems, including hallucinations, data privacy, intellectual property, and CBRN information risks. Provides practical guidance for organizations managing generative AI deployments.", "n": 3, "nn": "Extends existing NIST AI RMF with generative AI specifics; moderate novelty as it adapts established frameworks", "m": 5, "mn": "Official NIST publication with high maturity and credibility", "o": 4, "on": "Significant overlap with NIST AI RMF base framework and general AI risk management guidance", "mo": 4, "mon": "Recent publication (2024) indicates active momentum in NIST's AI governance work", "r": 5, "rn": "Highly relevant for AI governance, risk management, and compliance frameworks", "c": ["Policy/governance", "AI safety/alignment", "Other AI/agent"]}


draft_name: nist-sp-800-218a
prompt_hash: 7eb00d80e3226172
model: claude-haiku-4-5-20251001
request_json: batch[0]
created_at: 2026-03-08T17:22:33.019753+00:00
response_json:
  {"name": "nist-sp-800-218a", "s": "Community profile providing secure software development practices tailored to generative AI and dual-use foundation models, addressing data management, model training, and deployment security. Builds on NIST SSDF practices for AI-specific contexts.", "n": 3, "nn": "Adapts established SSDF practices to AI context; incremental novelty", "m": 4, "mn": "High maturity as official NIST special publication with security focus", "o": 4, "on": "Overlaps with NIST SP 800-218 base framework and general secure development practices", "mo": 4, "mon": "Recent 2024 publication shows sustained focus on AI security development", "r": 4, "rn": "Relevant for secure AI/ML development practices and supply chain security", "c": ["Policy/governance", "Model serving/inference", "Other AI/agent"]}


draft_name: nist-ir-8596
prompt_hash: a8b5fbb80611b14f
model: claude-haiku-4-5-20251001
request_json: batch[0]
created_at: 2026-03-08T17:22:33.029698+00:00
response_json:
  {"name": "nist-ir-8596", "s": "Community profile that maps NIST Cybersecurity Framework functions to AI-specific risks and controls, enabling organizations to manage cybersecurity risks in AI systems. Bridges existing CSF with AI risk landscape.", "n": 3, "nn": "Applies existing CSF to AI domain; moderate novelty in mapping and adaptation", "m": 4, "mn": "High maturity as NIST internal report with clear framework alignment", "o": 3, "on": "Moderate overlap with NIST CSF and other AI risk management frameworks, relatively distinct focus", "mo": 5, "mon": "Future-dated publication (2025) indicates planned momentum in CSF-AI integration", "r": 5, "rn": "Highly relevant for cybersecurity and AI risk management integration", "c": ["Policy/governance", "AI safety/alignment"]}


draft_name: nist-cswp-31
prompt_hash: 25ec9973684b2ce4
model: claude-haiku-4-5-20251001
request_json: batch[0]
created_at: 2026-03-08T17:22:33.038608+00:00
response_json:
  {"name": "nist-cswp-31", "s": "Describes a proxy design process for validation and verification of critical AI systems, addressing the challenge of testing complex and opaque AI systems. Provides practical methodologies for V&V in production AI environments.", "n": 4, "nn": "Addresses specific technical challenge of AI V&V with proxy-based approach; relatively novel", "m": 3, "mn": "Moderate maturity; technical guidance on emerging challenge with evolving best practices", "o": 2, "on": "Relatively unique focus on proxy validation/verification for AI systems", "mo": 3, "mon": "September 2024 publication shows emerging focus on this specific technical challenge", "r": 4, "rn": "Relevant for AI system testing, validation, and critical system assurance", "c": ["AI safety/alignment", "Model serving/inference", "Other AI/agent"]}


draft_name: nist-ir-8579
prompt_hash: b2f439da02d4ade3
model: claude-haiku-4-5-20251001
request_json: batch[0]
created_at: 2026-03-08T17:22:33.046596+00:00
response_json:
  {"name": "nist-ir-8579", "s": "Documents technical and security lessons learned from implementing an LLM-based chatbot at NCCOE, including prompt injection mitigations and practical security considerations. Provides real-world insights from production LLM deployment.", "n": 4, "nn": "Case study approach with practical learnings from actual LLM deployment; good novelty in applied context", "m": 4, "mn": "High maturity based on real implementation experience with concrete security findings", "o": 2, "on": "Relatively unique as implementation case study; limited direct overlap with framework documents", "mo": 4, "mon": "Future-dated (2025) indicates ongoing NCCOE LLM security work", "r": 4, "rn": "Relevant for LLM deployment security, prompt injection mitigation, and practical security guidance", "c": ["AI safety/alignment", "Human-agent interaction", "Model serving/inference"]}


draft_name: nist-nist-ai-agent-iam
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request_json: batch[0]
created_at: 2026-03-08T17:22:40.482218+00:00
response_json:
  {"name": "nist-nist-ai-agent-iam", "s": "Concept paper addressing identity and authorization mechanisms for software and AI agents, focusing on authentication, authorization, and accountability in automated systems. Establishes foundational guidance for agent identity management and access control.", "n": 4, "nn": "Highly novel in applying IAM principles specifically to AI agents; addresses emerging need as agents become more autonomous and interconnected.", "m": 2, "mn": "Early-stage concept paper (2026 date suggests forward-looking); limited practical implementations or matured standards yet available.", "o": 2, "on": "Minimal overlap with measurement/evaluation or RMF governance frameworks; focuses on distinct IAM domain.", "mo": 4, "mon": "Strong momentum indicated by NIST initiative and 2026 publication date; reflects industry shift toward agentic AI systems.", "r": 5, "rn": "Critical relevance for multi-agent systems, API security, and autonomous operations; directly addresses authentication/authorization gaps.", "c": ["Agent identity/auth", "Policy/governance", "Autonomous netops", "A2A protocols"]}


draft_name: nist-ai-100-6
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  {"name": "nist-ai-100-6", "s": "Guidance document on measurement and evaluation methodologies for assessing AI system trustworthiness across accuracy, fairness, robustness, and security dimensions. Provides structured approaches for AI system assessment.", "n": 3, "nn": "Builds on established evaluation frameworks but applies them comprehensively to trustworthiness; moderate novelty in integration of multiple quality dimensions.", "m": 4, "mn": "Mature guidance from established NIST AI RMF series (2024); based on consolidated industry practices and research.", "o": 3, "on": "Moderate overlap with RMF Playbook governance structures; complements rather than duplicates measurement guidance.", "mo": 4, "mon": "Solid momentum as part of active NIST AI 100 series; builds on established AI RMF adoption.", "r": 4, "rn": "Highly relevant for AI system validation, compliance, and risk management; applicable across diverse AI deployments.", "c": ["Policy/governance", "AI safety/alignment", "Model serving/inference", "Data formats/interop"]}


draft_name: nist-ai-100-1-playbook
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request_json: batch[0]
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  {"name": "nist-ai-100-1-playbook", "s": "Practical playbook companion to the NIST AI RMF providing actionable steps and suggested approaches for implementing governance, mapping, measurement, and management functions. Operationalizes the AI RMF framework.", "n": 2, "nn": "Limited novelty as implementation guide; primarily translates existing RMF concepts into practice rather than introducing new principles.", "m": 5, "mn": "Highly mature guidance from 2023; directly derived from established NIST AI RMF standard with proven adoption.", "o": 4, "on": "Significant overlap with AI 100-6 on measurement and governance; complementary rather than independent coverage.", "mo": 5, "mon": "Very strong momentum as foundational implementation guide; widely referenced in AI governance initiatives.", "r": 5, "rn": "Essential relevance for organizations implementing AI risk management; provides bridge between framework and operational practice.", "c": ["Policy/governance", "AI safety/alignment", "Autonomous netops", "Model serving/inference"]}


draft_name: draft-chen-secure-routing-use-cases
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created_at: 2026-03-08T17:26:27.908774+00:00
response_json:
  [{"title": "Security-Aware Routing Mechanism", "description": "Routing decisions incorporate security and trustworthiness assessments of links and forwarding nodes alongside traditional path metrics.", "type": "mechanism"}]


draft_name: draft-li-rtgwg-distributed-lossless-framework
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request_json: batch-ideas[draft-li-rtgwg-distributed-lossless-framework]
created_at: 2026-03-08T17:26:27.926443+00:00
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  [{"title": "Distributed AIDC Network Framework", "description": "A framework for lossless interconnection and reliable data transmission across multiple geographically distributed data centers to support large-scale distributed model training.", "type": "architecture"}]


draft_name: draft-sandowicz-httpbis-httpa2
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response_json:
  [{"title": "HTTP Attestable Version 2 Protocol", "description": "An HTTP extension enabling trusted communication through Trusted Execution Environment attestation without requiring TLS, treating TEEs as requestable Internet resources.", "type": "protocol"}]


draft_name: draft-toorop-dnsop-ranking-dns-data
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request_json: batch-ideas[draft-toorop-dnsop-ranking-dns-data]
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  [{"title": "Extended DNS Data Trustworthiness Ranking", "description": "An extended ranking system for DNS data trustworthiness that includes root server names/addresses and DNSSEC-verifiable data with assigned numerical values for comparison.", "type": "requirement"}]


draft_name: draft-voit-rats-trustworthy-path-routing
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request_json: batch-ideas[draft-voit-rats-trustworthy-path-routing]
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  [{"title": "Trusted Path Routing", "description": "A routing mechanism that forwards sensitive traffic exclusively through network devices that have been recently appraised and verified for trustworthiness.", "type": "mechanism"}]


draft_name: etsi-en-304-223
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created_at: 2026-03-08T17:26:30.570586+00:00
response_json:
  [{"title": "Baseline Cyber Security Requirements for AI", "description": "Establishes minimum security requirements and controls for AI model and system development, deployment, and operation.", "type": "requirement"}]


draft_name: etsi-gr-eni-007
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created_at: 2026-03-08T17:26:30.586894+00:00
response_json:
  [{"title": "AI Application Categories for Networks", "description": "Defines a taxonomy of categories for classifying how AI can be applied to network management and operations.", "type": "architecture"}]


draft_name: etsi-gr-sai-001
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created_at: 2026-03-08T17:26:30.592351+00:00
response_json:
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draft_name: etsi-gr-sai-002
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request_json: batch-ideas[etsi-gr-sai-002]
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response_json:
  [{"title": "Data Supply Chain Security", "description": "Defines security requirements and controls for managing data provenance, integrity, and integrity throughout the AI data supply chain.", "type": "requirement"}]


draft_name: etsi-gr-sai-003
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request_json: batch-ideas[etsi-gr-sai-003]
created_at: 2026-03-08T17:26:30.602507+00:00
response_json:
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draft_name: etsi-gr-sai-004
prompt_hash: 0bc73dd76f07d988
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request_json: batch-ideas[etsi-gr-sai-004]
created_at: 2026-03-08T17:26:33.642977+00:00
response_json:
  [{"title": "AI Decision-Making Risk Framework", "description": "Systematic identification and categorization of risks arising from automated decision-making systems in AI applications.", "type": "requirement"}, {"title": "Problem Taxonomy for AI Systems", "description": "Structured classification of technical and societal problems specific to AI and automated decision making.", "type": "pattern"}]


draft_name: etsi-gr-sai-005
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request_json: batch-ideas[etsi-gr-sai-005]
created_at: 2026-03-08T17:26:33.660144+00:00
response_json:
  [{"title": "AI Risk Mitigation Strategy", "description": "Comprehensive set of mitigation techniques and controls to address identified risks in AI and automated decision-making systems.", "type": "mechanism"}]


draft_name: etsi-gr-sai-006
prompt_hash: 5fab7686d8e14f63
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request_json: batch-ideas[etsi-gr-sai-006]
created_at: 2026-03-08T17:26:33.666528+00:00
response_json:
  [{"title": "Hardware-Based AI Security Architecture", "description": "Integration of hardware mechanisms and design principles to enhance security, trustworthiness, and safety of AI systems.", "type": "architecture"}]


draft_name: etsi-gs-eni-001
prompt_hash: f23b85b12812e7f8
model: claude-haiku-4-5-20251001
request_json: batch-ideas[etsi-gs-eni-001]
created_at: 2026-03-08T17:26:33.672290+00:00
response_json:
  [{"title": "ENI Application Use Cases", "description": "Concrete scenarios and requirements for deploying intent-based networking across diverse operational domains.", "type": "requirement"}]


draft_name: etsi-gs-eni-002
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request_json: batch-ideas[etsi-gs-eni-002]
created_at: 2026-03-08T17:26:33.677702+00:00
response_json:
  [{"title": "ENI System Requirements", "description": "Technical, functional, and operational requirements for end-to-end intent-based network infrastructure.", "type": "requirement"}]


draft_name: etsi-gs-eni-005
prompt_hash: 872b55e54681fa10
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request_json: batch-ideas[etsi-gs-eni-005]
created_at: 2026-03-08T17:26:36.307071+00:00
response_json:
  [{"title": "ENI System Architecture Framework", "description": "Defines the overall system architecture for Ericsson Network Intelligence with component interactions and data flows.", "type": "architecture"}]


draft_name: etsi-gs-eni-019
prompt_hash: 0a2ffe0041142a48
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created_at: 2026-03-08T17:26:36.313997+00:00
response_json:
  [{"title": "Context Information Representation and Inference", "description": "Establishes methods for representing, inferring, and applying contextual information within ENI systems for intelligent decision-making.", "type": "pattern"}]


draft_name: iec-np-ts-62366-3
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request_json: batch-ideas[iec-np-ts-62366-3]
created_at: 2026-03-08T17:26:36.319858+00:00
response_json:
  [{"title": "Usability Engineering Guidance for AI/ML Medical Devices", "description": "Provides guidance on applying usability engineering processes to medical devices that incorporate artificial intelligence and machine learning technology.", "type": "requirement"}]


draft_name: iso-12813-2015
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request_json: batch-ideas[iso-12813-2015]
created_at: 2026-03-08T17:26:36.325479+00:00
response_json:
  [{"title": "Compliance Check Communication Protocol for Fee Collection", "description": "Defines short-range communication protocol between vehicle on-board equipment and roadside interrogators to verify compliance with toll regime rules.", "type": "protocol"}]


draft_name: iso-12813-2019
prompt_hash: 64ba15775cdf98b0
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request_json: batch-ideas[iso-12813-2019]
created_at: 2026-03-08T17:26:38.982327+00:00
response_json:
  [{"title": "Compliance Check Communication Protocol", "description": "Short-range communication protocol between vehicle on-board equipment and roadside interrogators to verify that OBE-reported road usage data complies with toll regime rules.", "type": "protocol"}, {"title": "OBE, Vehicle, and Contract Identification", "description": "Mechanism to identify the on-board equipment, associated vehicle, and toll contract during compliance checking interactions.", "type": "mechanism"}]


draft_name: iso-12813-2024
prompt_hash: 2857834350e4a142
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request_json: batch-ideas[iso-12813-2024]
created_at: 2026-03-08T17:26:38.989008+00:00
response_json:
  [{"title": "Compliance Check Communication Protocol", "description": "Short-range communication protocol between vehicle on-board equipment and fixed/mobile roadside equipment or hand-held units to verify OBE-reported road usage data against toll regime rules.", "type": "protocol"}]


draft_name: iso-13141-2015
prompt_hash: 880652f1ac00aef4
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request_json: batch-ideas[iso-13141-2015]
created_at: 2026-03-08T17:26:38.996347+00:00
response_json:
  [{"title": "Localization Augmentation Communication Protocol", "description": "Short-range communication protocol between vehicle on-board equipment and fixed roadside equipment to provide location and heading information for autonomous electronic fee collection systems.", "type": "protocol"}, {"title": "Roadside Equipment Authentication", "description": "Security mechanism to protect on-board equipment from manipulation by false or unauthorized roadside equipment.", "type": "mechanism"}]


draft_name: iso-13141-2024
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request_json: batch-ideas[iso-13141-2024]
created_at: 2026-03-08T17:26:43.883664+00:00
response_json:
  [{"title": "Localization Augmentation Communication Protocol", "description": "Short-range communication protocol between on-board equipment and roadside equipment to augment vehicle localization and charge object identification in autonomous electronic fee collection systems.", "type": "protocol"}, {"title": "RSE Manipulation Protection Mechanism", "description": "Security mechanism to protect on-board equipment from false or manipulated location and heading information transmitted by roadside equipment.", "type": "mechanism"}]


draft_name: iso-16363-2012
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request_json: batch-ideas[iso-16363-2012]
created_at: 2026-03-08T17:26:43.900500+00:00
response_json:
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draft_name: iso-16363-2025
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request_json: batch-ideas[iso-16363-2025]
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response_json:
  [{"title": "Digital Repository Trustworthiness Audit Framework", "description": "CCSDS Recommended Practice for auditing and certifying trustworthiness of digital repositories applicable to the entire range of repository types.", "type": "framework"}]


draft_name: iso-16919-2014
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created_at: 2026-03-08T17:26:43.912522+00:00
response_json:
  [{"title": "Digital Repository Certification Body Requirements", "description": "Requirements and operational framework for organizations performing audit and certification of digital repositories using ISO 16363 metrics and assessment procedures.", "type": "requirement"}]


draft_name: iso-16919-2025
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response_json:
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draft_name: iso-17575-1-2016
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response_json:
  [{"title": "Configurable charge report generation", "description": "A mechanism for assembling charge reports from road usage data that can be configured per toll scheme according to local needs.", "type": "mechanism"}, {"title": "Front End to Back End data exchange protocol", "description": "A standardized protocol defining format and semantics for data exchange between OBE-based Front Ends and Back Ends in autonomous toll systems.", "type": "protocol"}]


draft_name: iso-17575-2-2016
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created_at: 2026-03-08T17:26:47.383066+00:00
response_json:
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draft_name: iso-17575-3-2016
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response_json:
  [{"title": "Toll context data specification", "description": "A data model defining content, semantics, and format for context data transmitted from Back End to Front End to configure charging processes for different tolling principles.", "type": "protocol"}]


draft_name: iso-17757-2017
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request_json: batch-ideas[iso-17757-2017]
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response_json:
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draft_name: iso-18374-2025
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request_json: batch-ideas[iso-18374-2025]
created_at: 2026-03-08T17:26:51.461264+00:00
response_json:
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draft_name: iso-18497-1-2024
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draft_name: iso-18497-2-2024
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response_json:
  [{"title": "Obstacle Protection System Design for Autonomous Agricultural Machinery", "description": "Specifies design principles for protective systems that detect and respond to obstacles in autonomous agricultural equipment.", "type": "mechanism"}]


draft_name: iso-18497-3-2024
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response_json:
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draft_name: iso-18497-4-2024
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response_json:
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draft_name: iso-18829-2017
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created_at: 2026-03-08T17:26:54.738137+00:00
response_json:
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draft_name: iso-19178-1-2025
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draft_name: iso-20682-2026
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response_json:
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draft_name: iso-22133-2026
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created_at: 2026-03-08T17:26:54.764972+00:00
response_json:
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draft_name: iso-22373-2025
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  [{"title": "Supply Chain Trustworthiness Framework", "description": "A framework with guidelines and interoperable data structures for stakeholders to exchange and establish trustworthiness information across supply and value chains.", "type": "framework"}]


draft_name: iso-22733-1-2021
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draft_name: iso-22733-2-2023
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draft_name: iso-iec-dis-30754
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draft_name: iso-iec-dis-42105
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draft_name: iso-iec-dtr-23888-3.2
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draft_name: iso-iec-dts-42112
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draft_name: iso-iec-dts-42119-3.2
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draft_name: iso-iec-np-25880
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draft_name: iso-iec-np-26582
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draft_name: iso-iec-np-ts-26312
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draft_name: iso-iec-np-ts-42110
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draft_name: iso-iec-pwi-18966
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draft_name: iso-iec-pwi-20822
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draft_name: iso-iec-pwi-26200
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draft_name: iso-iec-pwi-5957
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draft_name: iso-iec-pwi-7699
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draft_name: iso-iec-pwi-9991
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draft_name: iso-iec-tr-17903-2024
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draft_name: iso-iec-tr-20226-2025
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draft_name: iso-iec-tr-20547-1-2020
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draft_name: iso-iec-tr-20547-2-2018
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draft_name: iso-iec-tr-20547-5-2018
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draft_name: iso-iec-tr-21221-2025
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draft_name: iso-iec-tr-24027-2021
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draft_name: iso-iec-tr-24028-2020
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draft_name: iso-iec-tr-24029-1-2021
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draft_name: iso-iec-tr-24368-2022
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draft_name: iso-iec-tr-24372-2021
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draft_name: iso-iec-tr-27563-2023
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draft_name: iso-iec-tr-42106
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draft_name: iso-iec-tr-5259-6
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draft_name: iso-iec-ts-42119-2-2025
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draft_name: iso-np-20319
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draft_name: iso-np-26604-1
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draft_name: iso-np-pas-8800-2
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model: claude-haiku-4-5-20251001
request_json: batch-ideas[nist-ai-100-2-e2025]
created_at: 2026-03-08T17:29:27.550224+00:00
response_json:
  [{"title": "Adversarial ML Attack and Mitigation Taxonomy", "description": "A systematic classification of adversarial machine learning attacks (evasion, poisoning, privacy) and corresponding mitigations applicable to both predictive and generative AI systems.", "type": "pattern"}]


draft_name: nist-ai-100-4
prompt_hash: 3d74aec0b56d5e7d
model: claude-haiku-4-5-20251001
request_json: batch-ideas[nist-ai-100-4]
created_at: 2026-03-08T17:29:27.556087+00:00
response_json:
  [{"title": "Synthetic Content Risk Management Approaches", "description": "A set of approaches including authentication, provenance tracking, and content labeling to manage risks from AI-generated synthetic content.", "type": "mechanism"}]


draft_name: nist-ai-100-6
prompt_hash: b6cfd3e9e1311484
model: claude-haiku-4-5-20251001
request_json: batch-ideas[nist-ai-100-6]
created_at: 2026-03-08T17:29:27.562497+00:00
response_json:
  [{"title": "AI Trustworthiness Measurement Framework", "description": "Guidance on measurement and evaluation methodologies for assessing AI system trustworthiness across dimensions including accuracy, fairness, robustness, and security.", "type": "protocol"}]


draft_name: nist-ai-600-1
prompt_hash: 7cb97fde9990b143
model: claude-haiku-4-5-20251001
request_json: batch-ideas[nist-ai-600-1]
created_at: 2026-03-08T17:29:29.158290+00:00
response_json:
  [{"title": "Generative AI Risk Management Profile", "description": "A specialized risk management framework extension that identifies and addresses risks unique to or exacerbated by generative AI systems, including hallucinations, data privacy, intellectual property, and CBRN information exposure.", "type": "architecture"}, {"title": "Hallucination Risk Characterization", "description": "A systematic approach to identifying, measuring, and mitigating false or fabricated outputs generated by language models and other generative systems.", "type": "pattern"}]


draft_name: draft-li-rtgwg-distributed-lossless-framework
prompt_hash: 4a7716ef00a743c2
model: claude-haiku-4-5-20251001
request_json: batch[0]
created_at: 2026-03-08T17:31:23.089767+00:00
response_json:
  {"name": "draft-li-rtgwg-distributed-lossless-framework", "s": "Proposes a framework for efficient lossless interconnection between multiple data centers to support distributed LLM training at scale. Addresses key technologies for connecting data centers into larger clusters while ensuring reliable data transmission.", "n": 3, "nn": "Distributed data center networking for AI is established; framework contribution is incremental application of existing principles.", "m": 2, "mn": "Early-stage framework lacking detailed protocol specifications, implementation details, or validation results.", "o": 3, "on": "Overlaps with general distributed systems and data center networking literature; some connection to ML traffic management themes.", "mo": 2, "mon": "Limited evidence of community engagement or iterative development in document history.", "r": 3, "rn": "Relevant to AI infrastructure but narrowly scoped to interconnection layer; may have limited applicability outside large-scale training scenarios.", "c": ["ML traffic mgmt", "Model serving/inference"]}


draft_name: draft-voit-rats-trustworthy-path-routing
prompt_hash: 1b9cb22fe8bde7dc
model: claude-haiku-4-5-20251001
request_json: batch[0]
created_at: 2026-03-08T17:31:23.111679+00:00
response_json:
  {"name": "draft-voit-rats-trustworthy-path-routing", "s": "Describes Trusted Path Routing mechanism that forwards sensitive network traffic through devices recently verified as trustworthy using remote attestation. Combines routing decisions with device trustworthiness appraisal to protect confidential data flows.", "n": 4, "nn": "Novel integration of remote attestation (RATS) into routing decisions; represents new approach to security-aware path selection.", "m": 3, "mn": "Mature specification document with clear abstractions; some implementation guidance present but real-world deployment experience unclear.", "o": 2, "on": "Minimal overlap with other drafts; distinct focus on attestation-aware routing rather than general secure routing.", "mo": 4, "mon": "Substantial document length (32pg) and technical depth suggest active development; author (Voit) is known RATS contributor.", "r": 4, "rn": "Highly relevant to emerging security requirements where encryption alone deemed insufficient; applicable to high-sensitivity government and enterprise networks.", "c": ["Agent identity/auth", "Policy/governance", "Autonomous netops"]}


draft_name: draft-toorop-dnsop-ranking-dns-data
prompt_hash: 27d5c173daacfb89
model: claude-haiku-4-5-20251001
request_json: batch[0]
created_at: 2026-03-08T17:31:23.121982+00:00
response_json:
  {"name": "draft-toorop-dnsop-ranking-dns-data", "s": "Extends RFC2181's DNS data trustworthiness ranking by adding entries for root server names, built-in resolver addresses, and DNSSEC-verified data with assigned numeric values. Provides standardized reference framework for comparing DNS data reliability.", "n": 2, "nn": "Incremental extension of established DNS ranking; primarily organizational refinement rather than novel security mechanism.", "m": 4, "mn": "Well-grounded in existing DNS standards (RFC2181); clear operationalization of trustworthiness rankings with practical applicability.", "o": 1, "on": "Highly specialized DNS domain; minimal overlap with other networking or AI-related drafts.", "mo": 3, "mon": "Modest document scope (4pg) suggests focused, bounded contribution; unclear iteration frequency.", "r": 2, "rn": "Limited relevance to AI systems and distributed agent networks; primarily DNS operational utility.", "c": ["Data formats/interop", "Policy/governance"]}


draft_name: draft-sandowicz-httpbis-httpa2
prompt_hash: 1e1935fcfaf363b5
model: claude-haiku-4-5-20251001
request_json: batch[0]
created_at: 2026-03-08T17:31:23.132231+00:00
response_json:
  {"name": "draft-sandowicz-httpbis-httpa2", "s": "Extends HTTP with remote attestation capabilities (HTTPA/2) enabling trusted communication via Trusted Execution Environments without requiring TLS. Treats TEE as a requestable resource and allows endpoints to verify each other's trustworthiness through attestation evidence.", "n": 4, "nn": "Novel decoupling of attestation from TLS; interesting approach treating TEE attestation as core protocol feature rather than auxiliary security layer.", "m": 3, "mn": "Detailed specification (34pg) with clear TEE definitions; lacks discussion of standardization status, reference implementations, or deployment considerations.", "o": 2, "on": "Related to attestation themes in trusted-path-routing draft but distinct protocol layer (HTTP vs routing); complementary rather than overlapping.", "mo": 3, "mon": "Substantial technical document suggesting serious effort; unclear whether follow-up versions or community feedback exist.", "r": 4, "rn": "Relevant to agent identity/auth and secure agent communication; applicable to scenarios requiring strong mutual verification of remote execution environments.", "c": ["Agent identity/auth", "A2A protocols", "Model serving/inference"]}


draft_name: draft-chen-secure-routing-use-cases
prompt_hash: 83e8562e92992065
model: claude-haiku-4-5-20251001
request_json: batch[0]
created_at: 2026-03-08T17:31:23.142062+00:00
response_json:
  {"name": "draft-chen-secure-routing-use-cases", "s": "Proposes adding security and trustworthiness factors to routing decisions beyond shortest-path algorithms. Presents use cases for secure routing that converges routing with ISP security infrastructure to ensure secure data transmission.", "n": 3, "nn": "Security-aware routing concept is established; contribution primarily frames use cases rather than novel mechanisms.", "m": 2, "mn": "Early-stage use case document (9pg) lacking detailed protocol design, threat modeling, or implementation validation.", "o": 4, "on": "Substantial thematic overlap with trusted-path-routing draft; both address security-aware routing but from different angles (use cases vs mechanisms).", "mo": 2, "mon": "Short, foundational document suggests early exploration phase; limited evidence of detailed technical development.", "r": 3, "rn": "Moderately relevant to autonomous network operations and security policy integration; applicable to enterprise and ISP scenarios.", "c": ["Policy/governance", "Autonomous netops"]}


draft_name: nist-cswp-31
prompt_hash: 81abffcdf4463aee
model: claude-haiku-4-5-20251001
request_json: batch-ideas[nist-cswp-31]
created_at: 2026-03-08T17:34:56.230381+00:00
response_json:
  [{"title": "Proxy Validation and Verification Framework", "description": "A design process for validating and verifying critical AI systems through proxy mechanisms that address opacity and complexity challenges in AI testing.", "type": "mechanism"}]


draft_name: nist-ir-8579
prompt_hash: bf34f8debbd9dac4
model: claude-haiku-4-5-20251001
request_json: batch-ideas[nist-ir-8579]
created_at: 2026-03-08T17:34:56.247330+00:00
response_json:
  [{"title": "Prompt Injection Mitigation Techniques", "description": "Practical mitigations and defenses against prompt injection attacks demonstrated through implementation of an LLM-based chatbot in production.", "type": "pattern"}]


draft_name: nist-ir-8596
prompt_hash: 778906c0e28d1414
model: claude-haiku-4-5-20251001
request_json: batch-ideas[nist-ir-8596]
created_at: 2026-03-08T17:34:56.253228+00:00
response_json:
  [{"title": "NIST Cybersecurity Framework Profile for AI", "description": "A mapping of NIST CSF functions to AI-specific cybersecurity risks and controls, enabling organizations to apply existing framework structures to AI systems.", "type": "architecture"}]


draft_name: nist-nist-ai-agent-iam
prompt_hash: 3414915d1cade3af
model: claude-haiku-4-5-20251001
request_json: batch-ideas[nist-nist-ai-agent-iam]
created_at: 2026-03-08T17:34:56.259204+00:00
response_json:
  [{"title": "AI Agent Identity and Authorization Framework", "description": "A conceptual framework for authentication, authorization, and accountability mechanisms specific to autonomous software and AI agents in automated systems.", "type": "protocol"}]


draft_name: nist-sp-800-218a
prompt_hash: e856de164d3fa480
model: claude-haiku-4-5-20251001
request_json: batch-ideas[nist-sp-800-218a]
created_at: 2026-03-08T17:34:56.264947+00:00
response_json:
  [{"title": "SSDF Profile for Generative AI and Foundation Models", "description": "An SSDF community profile defining secure software development practices tailored to generative AI and dual-use foundation models, covering data, training, and deployment security.", "type": "architecture"}]


draft_name: _landscape_
prompt_hash: 776d2af46a9dcc72
model: claude-sonnet-4-20250514
created_at: 2026-03-08T17:40:43.085948+00:00
request_json:
  You are analyzing the landscape of 761 documents related to AI agents and autonomous systems from multiple standards organizations.

  ## IETF Drafts — Categories and Draft Counts
  - Policy/governance: 247 drafts
  - Data formats/interop: 206 drafts
  - Agent identity/auth: 152 drafts
  - AI safety/alignment: 143 drafts
  - A2A protocols: 137 drafts
  - Autonomous netops: 108 drafts
  - Agent discovery/reg: 81 drafts
  - Model serving/inference: 67 drafts
  - Human-agent interaction: 65 drafts
  - ML traffic mgmt: 65 drafts
  - Other AI/agent: 64 drafts

  ## Most Common Technical Ideas (from IETF drafts)
  - AC/TE YANG Models for Edge AI Placement (3 drafts)
  - Model Context Protocol over MOQT (3 drafts)
  - Automatic Extended Route Optimization over OMNI Interfaces (3 drafts)
  - A2A Protocol Transport over MOQT (2 drafts)
  - AI Traffic Characterization Framework (2 drafts)
  - Federated AI Service Operations Model (2 drafts)
  - Intent-Based Security Policy Translation for 5G (2 drafts)
  - Distributed Policy Enforcement via NEF and IBN Controllers (2 drafts)
  - Hybrid Post-Quantum Cryptography for EAP-AKA' (2 drafts)
  - Tiered Risk-Based Governance for Autonomous AI Agents (2 drafts)
  - Hybrid Public Key Encryption Scheme (2 drafts)
  - DH-KA derived HMAC for JWS (2 drafts)
  - AI Agent Protocol Framework (2 drafts)
  - HTTP Content Usage Preference Signaling (2 drafts)
  - KEM-based Authentication for EDHOC (2 drafts)
  - PIE Queue Management Scheme (2 drafts)
  - Human-in-the-Loop Confirmation Protocol (2 drafts)
  - Intent-based Agent Interconnection Protocol (2 drafts)
  - Decentralized LLM Inference Coordination Framework (2 drafts)
  - DRIP Entity Tags for Air Domain Awareness (2 drafts)

  ## Known Overlap Clusters (groups of highly similar drafts)
  Multiple clusters of near-duplicate drafts exist, particularly in:
  - Policy/governance (247 drafts, high internal overlap)
  - Data formats/interop (206 drafts, high internal overlap)
  - Agent identity/auth (152 drafts, high internal overlap)
  - AI safety/alignment (143 drafts, high internal overlap)
  - A2A protocols (137 drafts, high internal overlap)


  ## Other Standards Bodies

  ### ETSI (12 documents)
  - **ETSI EN 304 223: Baseline Cyber Security Requirements for AI Models and Systems**: ETSI SAI specification: Baseline Cyber Security Requirements for AI Models and Systems
  - **ETSI GR ENI 007: ENI Definition of Categories for AI Application to Networks**: ETSI ENI specification: ENI Definition of Categories for AI Application to Networks
  - **ETSI GR SAI 001: AI Threat Ontology**: ETSI SAI specification: AI Threat Ontology
  - **ETSI GR SAI 002: Data Supply Chain Security**: ETSI SAI specification: Data Supply Chain Security
  - **ETSI GR SAI 003: Security Testing of AI**: ETSI SAI specification: Security Testing of AI
  - **ETSI GR SAI 004: Problem Statement on AI and Automated Decision Making**: ETSI SAI specification: Problem Statement on AI and Automated Decision Making
  - **ETSI GR SAI 005: Mitigation Strategy Report**: ETSI SAI specification: Mitigation Strategy Report
  - **ETSI GR SAI 006: Role of Hardware in AI Security**: ETSI SAI specification: Role of Hardware in AI Security
  - **ETSI GS ENI 001: ENI Use Cases**: ETSI ENI specification: ENI Use Cases
  - **ETSI GS ENI 002: ENI Requirements**: ETSI ENI specification: ENI Requirements
  - **ETSI GS ENI 005: System Architecture**: ETSI ENI specification: System Architecture
  - **ETSI GS ENI 019: Representing, Inferring, and Applying Context Information**: ETSI ENI specification: Representing, Inferring, and Applying Context Information

  ### ISO (238 documents)
  - **IEC/NP TS 62366-3: Medical devices — Part 3: Guidance on the application of usability engineering to**: This Part of IEC 62366, which is a Technical Specification, contains background information and provides guidance that addresses the specific areas th
  - **ISO 12813:2015: Electronic fee collection — Compliance check communication for autonomous systems**: <p>ISO 12813:2015 defines requirements for short-range communication for the purposes of compliance checking in autonomous electronic fee-collecting s
  - **ISO 12813:2015/Amd 1:2017: Electronic fee collection — Compliance check communication for autonomous**: ISO/IEC standard: Electronic fee collection — Compliance check communication for autonomous systems — Amendment 1. Committee: ISO/TC 204.
  - **ISO 12813:2015/CD Amd 2: Electronic fee collection — Compliance check communication for autonomous s**: ISO/IEC standard: Electronic fee collection — Compliance check communication for autonomous systems — Amendment 2. Committee: ISO/TC 204.
  - **ISO 12813:2019: Electronic fee collection — Compliance check communication for autonomous systems**: <p>This document defines requirements for short-range communication for the purposes of compliance checking in autonomous electronic fee collecting sy
  - **ISO 12813:2024: Electronic fee collection — Compliance check communication for autonomous systems**: <p class='MsoBodyText'><span lang='EN-GB'>This document specifies requirements for short-range communication for the purposes of compliance checking i
  - **ISO 13141:2015: Electronic fee collection — Localisation augmentation communication for autonomous s**: <p>ISO 13141:2015 establishes requirements for short-range communication for the purposes of augmenting the localization in autonomous electronic fee 
  - **ISO 13141:2015/Amd 1:2017: Electronic fee collection — Localisation augmentation communication for a**: ISO/IEC standard: Electronic fee collection — Localisation augmentation communication for autonomous systems — Amendment 1. Committee: ISO/TC 204.
  - **ISO 13141:2024: Electronic fee collection — Localization augmentation communication for autonomous s**: <p class='MsoBodyText'><span lang='EN-GB'>This document establishes requirements for short-range communication for the purposes of augmenting the loca
  - **ISO 16363:2012: Space data and information transfer systems — Audit and certification of trustworthy**: <p>ISO 16363:2012 defines a recommended practice for assessing the trustworthiness of digital repositories. It is applicable to the entire range of di
  - **ISO 16363:2025: Space data and information transfer systems — Audit and certification of trustworthy**: <p class='MsoNormal'><span style='color: black; mso-color-alt: windowtext;'>This document defines a CCSDS Recommended Practice on which to base an aud
  - **ISO 16919:2014: Space data and information transfer systems — Requirements for bodies providing audi**: <p>ISO 16919:2014 is meant primarily for those setting up and managing the organization performing the auditing and certification of digital repositor
  - **ISO 16919:2025: Space data and information transfer systems — Requirements for bodies providing audi**: <p class='MsoNormal'><span lang='EN-GB' style='mso-ansi-language: EN-GB;'>This document defines a CCSDS Recommended Practice (and ISO standard) on whi
  - **ISO 17575-1:2016: Electronic fee collection — Application interface definition for autonomous system**: <p>ISO 17575-1:2016 defines the format and semantics of the data exchange between a Front End (OBE plus optional proxy) and corresponding Back Ends in
  - **ISO 17575-2:2016: Electronic fee collection — Application interface definition for autonomous system**: <p>ISO 17575-2:2016 defines how to convey all or parts of the data element structure defined in other parts of ISO 17575 over any communication stack 
  - **ISO 17575-3:2016: Electronic fee collection — Application interface definition for autonomous system**: <p>ISO 17575-3:2016 defines the content, semantics and format of the data exchange between a Front End (OBE plus optional proxy) and the corresponding
  - **ISO 17757:2017: Earth-moving machinery and mining — Autonomous and semi-autonomous machine system sa**: <p>ISO 17757 provides safety requirements for autonomous machines and semi-autonomous machines used in earth-moving and mining operations, and their a
  - **ISO 17757:2019: Earth-moving machinery and mining — Autonomous and semi-autonomous machine system sa**: <p>This document provides safety requirements for autonomous machines and semi-autonomous machines (ASAM) used in earth-moving and mining operations, 
  - **ISO 18374:2025: Dentistry — Artificial intelligence (AI) and augmented intelligence (AuI) based 2D r**: <p class='MsoBodyText'><span lang='EN-GB'>This document defines the requirements for developing and documenting the goals, limitations, target end use
  - **ISO 18497-1:2024: Agricultural machinery and tractors — Safety of partially automated, semi-autonomo**: <p class='MsoBodyText'><span lang='EN-GB'>This document specifies principles for the design of agricultural machinery and tractors that are used in ag
  - **ISO 18497-2:2024: Agricultural machinery and tractors — Safety of partially automated, semi-autonomo**: <p class='MsoBodyText'><span lang='EN-GB'>This document specifies principles for the design of obstacle protective systems used in agricultural machin
  - **ISO 18497-3:2024: Agricultural machinery and tractors — Safety of partially automated, semi-autonomo**: <p class='MsoBodyText'><span lang='EN-GB'>This document specifies principles for the design of agricultural machinery and tractors utilizing systems (
  - **ISO 18497-4:2024: Agricultural machinery and tractors — Safety of partially automated, semi-autonomo**: <p class='MsoBodyText'><span lang='EN-GB'>This document specifies principles for verification methods and validation principles of agricultural machin
  - **ISO 18829:2017: Document management — Assessing ECM/EDRM implementations — Trustworthiness**: <p>ISO 18829:2017 identifies activities and operations that an organization needs to perform, or have performed, to evaluate whether the electronicall
  - **ISO 19178-1:2025: Geographic information — Training data markup language for artificial intelligence**: <p class='MsoBodyText'><span lang='EN-GB'>Within the context of training data for Earth Observation (EO) Artificial Intelligence Machine Learning (AI/
  - **ISO 20682:2026: Autonomous underwater vehicles — Risk and reliability**: <p><span lang='EN-GB'>This document specifies the risks and reliability of an autonomous underwater vehicle (AUV) in its underwater realm. It covers s
  - **ISO 22133:2026: Road vehicles — Test object monitoring and control for active safety and automated/a**: <p><span lang='EN-GB'>This document specifies requirements, procedures and message formats for controlling and monitoring of test objects, used for te
  - **ISO 22373:2025: Security and resilience — Authenticity, integrity and trust for products and documen**: <p class='MsoBodyText'><span lang='EN-GB'>This document establishes a framework to support stakeholders in supply and value chains to ensure the chain
  - **ISO 22733-1:2021: Road vehicles — Test method to evaluate the performance of autonomous emergency br**: <p>This document specifies a method to evaluate the behaviour of a vehicle equipped with an autonomous emergency braking system (AEBS), or dynamic bra
  - **ISO 22733-1:2022: Road vehicles — Test method to evaluate the performance of autonomous emergency br**: <p>This document specifies a method to evaluate the behaviour of a vehicle equipped with an autonomous emergency braking system (AEBS), or dynamic bra
    ... and 208 more

  ### ITU (20 documents)
  - **ITU-T FG-AI4H DEL01: AI for Health: Ethics and Governance**: ITU-T Recommendation FG-AI4H DEL01 on AI health: AI for Health: Ethics and Governance
  - **ITU-T FG-AI4H DEL02: AI for Health: Data handling**: ITU-T Recommendation FG-AI4H DEL02 on AI health: AI for Health: Data handling
  - **ITU-T FG-AI4H DEL7.2: AI for Health: Clinical evaluation of AI**: ITU-T Recommendation FG-AI4H DEL7.2 on AI health: AI for Health: Clinical evaluation of AI
  - **ITU-T X.1381: Framework for AI risk assessment in telecommunication networks**: ITU-T Recommendation X.1381 on AI security: Framework for AI risk assessment in telecommunication networks
  - **ITU-T X.1382: Security requirements for AI-based solutions in telecommunication networks**: ITU-T Recommendation X.1382 on AI security: Security requirements for AI-based solutions in telecommunication networks
  - **ITU-T X.1383: Assessment criteria for trustworthiness of AI-based telecommunication services**: ITU-T Recommendation X.1383 on Trustworthy AI: Assessment criteria for trustworthiness of AI-based telecommunication services
  - **ITU-T X.1384: Security threats and risk treatment measures for AI-based telecommunication systems**: ITU-T Recommendation X.1384 on AI security: Security threats and risk treatment measures for AI-based telecommunication systems
  - **ITU-T Y.3172: Architectural framework for machine learning in future networks including IMT-2020**: ITU-T Recommendation Y.3172 on AI/ML architecture: Architectural framework for machine learning in future networks including IMT-2020
  - **ITU-T Y.3173: Framework for evaluating intelligence levels of future networks including IMT-2020**: ITU-T Recommendation Y.3173 on AI/ML architecture: Framework for evaluating intelligence levels of future networks including IMT-2020
  - **ITU-T Y.3174: Framework for data handling to enable machine learning in future networks including IM**: ITU-T Recommendation Y.3174 on AI/ML architecture: Framework for data handling to enable machine learning in future networks including IMT-2020
  - **ITU-T Y.3176: Machine learning marketplace integration in future networks including IMT-2020**: ITU-T Recommendation Y.3176 on AI/ML architecture: Machine learning marketplace integration in future networks including IMT-2020
  - **ITU-T Y.3177: Architectural framework of AI-as-a-Service to enable AI services in future networks**: ITU-T Recommendation Y.3177 on AI/ML architecture: Architectural framework of AI-as-a-Service to enable AI services in future networks
  - **ITU-T Y.3178: Requirements and framework of federated machine learning**: ITU-T Recommendation Y.3178 on Federated learning: Requirements and framework of federated machine learning
  - **ITU-T Y.3179: Architectural framework for AI-based network automation**: ITU-T Recommendation Y.3179 on Network automation: Architectural framework for AI-based network automation
  - **ITU-T Y.3180: Framework for multi-domain ML pipeline in future networks**: ITU-T Recommendation Y.3180 on AI/ML architecture: Framework for multi-domain ML pipeline in future networks
  - **ITU-T Y.3181: Architectural framework for trustworthy networking based on machine learning technolog**: ITU-T Recommendation Y.3181 on Trustworthy AI: Architectural framework for trustworthy networking based on machine learning technology
  - **ITU-T Y.3530: Cloud computing — Overview of machine learning in future networks**: ITU-T Recommendation Y.3530 on Cloud AI: Cloud computing — Overview of machine learning in future networks
  - **ITU-T Y.3531: Cloud computing — Functional architecture for machine learning as a service**: ITU-T Recommendation Y.3531 on Cloud AI: Cloud computing — Functional architecture for machine learning as a service
  - **ITU-T Y.4464: Framework for IoT-area network using autonomous agents**: ITU-T Recommendation Y.4464 on IoT agents: Framework for IoT-area network using autonomous agents
  - **ITU-T Y.4907: Reference architecture for intelligent transportation systems communication network us**: ITU-T Recommendation Y.4907 on AI transport: Reference architecture for intelligent transportation systems communication network using AI

  ### NIST (11 documents)
  - **NIST AI 100-1: Artificial Intelligence Risk Management Framework (AI RMF 1.0)**: The AI RMF provides a framework for managing risks associated with AI systems throughout their lifecycle, addressing characteristics of trustworthy AI
  - **NIST AI 100-1 Playbook: AI RMF Playbook**: Practical companion to the AI RMF providing suggested actions for achieving AI risk management outcomes, organized by the framework's functions: Gover
  - **NIST AI 100-2 E2025: Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigat**: Establishes a taxonomy of adversarial machine learning attacks and mitigations, covering evasion, poisoning, and privacy attacks against predictive AI
  - **NIST AI 100-4: Reducing Risks Posed by Synthetic Content**: Provides guidance on approaches to manage risks from synthetic content generated by AI, including authentication, provenance tracking, and content lab
  - **NIST AI 100-6: AI Measurement and Evaluation**: Guidance on measurement and evaluation approaches for AI systems to assess trustworthiness characteristics including accuracy, fairness, robustness, a
  - **NIST AI 600-1: Artificial Intelligence Risk Management Framework: Generative AI Profile**: Companion resource for the AI RMF focusing on risks unique to or exacerbated by generative AI, covering hallucinations, data privacy, intellectual pro
  - **NIST CSWP 31: Proxy Validation and Verification for Critical AI Systems**: Describes a proxy design process for validation and verification of critical AI systems, addressing challenges of testing AI systems that are complex 
  - **NIST IR 8579: Developing the NCCoE Chatbot: Technical and Security Learnings**: Documents technical and security lessons from implementing an LLM-based chatbot at the National Cybersecurity Center of Excellence, including prompt i
  - **NIST IR 8596: Cybersecurity Framework Profile for Artificial Intelligence**: A community profile mapping NIST Cybersecurity Framework functions to AI-specific risks and controls, helping organizations manage cybersecurity risks
  - **NIST NIST-AI-Agent-IAM: Accelerating the Adoption of Software and AI Agent Identity and Authorizatio**: Concept paper on identity and authorization for AI agents, addressing how software agents authenticate, authorize, and maintain accountability in auto
  - **NIST SP 800-218A: Secure Software Development Practices for Generative AI and Dual-Use Foundation Mo**: An SSDF community profile providing secure software development practices specific to generative AI and dual-use foundation models, addressing data ma

  ### W3C (11 documents)
  - **User Agent Accessibility Guidelines 1.0**: This document provides guidelines for designing user agents that lower barriers to Web accessibility for people with disabilities (visual, hearing, ph
  - **Techniques for User Agent Accessibility Guidelines 1.0**: This document provides techniques for satisfying the checkpoints defined in "User Agent Accessibility Guidelines 1.0" [UAAG10] . These techniques addr
  - **User Agent Accessibility Guidelines (UAAG) 2.0**: This document provides guidelines for designing user agents that lower barriers to Web accessibility for people with disabilities. User agents include
  - **UAAG 2.0 Reference: Explanations, Examples, and Resources for User Agent Accessibility Guidelines 2.**: This document provides explanation of the intent of UAAG 2.0 success criteria, examples of implementation of the UAAG 2.0 guidelines, best practice re
  - **User Agent Accessibility Guidelines 2.0 Requirements**: This First Public Working Draft outlines the requirements that the User Agent Accessibility Guidelines Working Group (UAWG) has set for development of
  - **Client-Specific Web Services by Using User Agent Attributes**: This document describes a simple framework for realizing client-specific web services. The proposed method uses the client properties such as screen s
  - **User Agent Authentication Forms**: This note proposes a new HTML capability to aid in the development of authenticated web user interfaces.
  - **Common User Agent Problems**: This document explains some common mistakes in user agents due to incorrect or incomplete implementation of specifications, and suggests remedies. It 
  - **Imagemapped Images and Image-Incapable User Agents**: This memo discusses appropriate handling of documents containing imagemapped images in cases where the user-agent cannot display or otherwise process 
  - **WAI-ARIA 1.0 User Agent Implementation Guide**: Describes how user agents should support keyboard navigation, respond to roles, states, and properties provided in Web content via WAI-ARIA, and expos
  - **Web User Agents**: Web user agents include both web browsers and other intermediaries between end-users and the web. Each user agent serves its user, not any of the othe

  Identify 8-15 GAPS — areas, problems, or technical challenges NOT adequately addressed by existing drafts and standards.

  Return a JSON array:
  [{"topic":"short topic name","description":"2-3 sentence description","category":"closest category or new","severity":"critical|high|medium|low","evidence":"what suggests this gap matters","addressed_by":"which existing standards (if any) partially address this, from any source"}]

  Focus on:
  1. Problems mentioned but not solved — even across organizations
  2. Missing infrastructure pieces (no standard from ANY body covers it)
  3. Security/privacy/safety issues not addressed
  4. Interoperability gaps between competing proposals or between standards bodies
  5. Real-world deployment concerns ignored
  6. Cross-organization coordination gaps (e.g., IETF protocol needs ISO governance framework)

  Consider what NIST, ISO, ETSI, ITU-T, and W3C already cover vs what remains missing.
  JSON array only, no fences.

response_json:
  [
    {
      "topic": "Cross-organizational AI agent liability",
      "description": "When AI agents from different organizations interact autonomously and cause harm, there's no standard framework for determining liability chains or insurance coverage across organizational boundaries. This becomes critical as agents make decisions that span multiple jurisdictions and technical domains.",
      "category": "Policy/governance",
      "severity": "critical",
      "evidence": "High overlap in IETF governance drafts suggests unsolved problems, while ISO focuses on single-organization safety and NIST on risk management but not cross-org liability",
      "addressed_by": "NIST AI RMF provides single-org risk management, ISO 22373 covers supply chain trust, but none address multi-agent liability chains"
    },
    {
      "topic": "Real-time AI agent explainability protocols",
      "description": "No standard exists for AI agents to provide real-time explanations of their decisions to other agents or humans during critical operations. Current explainability frameworks are post-hoc and unsuitable for time-sensitive autonomous operations.",
      "category": "Human-agent interaction",
      "severity": "high",
      "evidence": "Only 65 IETF drafts on human-agent interaction vs 247 on governance, suggesting focus on control rather than understanding during operation",
      "addressed_by": "NIST AI 100-6 covers evaluation but not real-time explanation, ITU-T Y.3181 addresses trustworthy networking but not explanation protocols"
    },
    {
      "topic": "Emergency shutdown coordination across agent networks",
      "description": "When cascading failures occur in interconnected AI agent systems, there's no standardized protocol for coordinated emergency shutdown that preserves critical services while stopping harmful propagation. Current safety standards focus on individual system shutdown.",
      "category": "AI safety/alignment",
      "severity": "critical",
      "evidence": "High internal overlap in AI safety drafts suggests competing approaches without consensus on network-wide emergency procedures",
      "addressed_by": "ISO 17757 covers autonomous machine safety, ITU-T X.1384 addresses AI security threats, but neither covers coordinated network shutdown"
    },
    {
      "topic": "AI agent resource consumption governance",
      "description": "No framework exists for managing and limiting the computational, energy, and network resources that autonomous AI agents can consume, especially during resource contention or emergency situations. This is critical for preventing resource monopolization by misbehaving agents.",
      "category": "Autonomous netops",
      "severity": "high",
      "evidence": "Only 65 IETF drafts on ML traffic management vs much larger numbers on protocols, indicating insufficient focus on resource governance",
      "addressed_by": "ITU-T Y.3172 provides ML architectural framework but doesn't address resource governance, IETF has traffic management drafts but not AI-specific resource limits"
    },
    {
      "topic": "Cross-domain AI agent identity federation",
      "description": "While individual identity standards exist, there's no framework for AI agents to maintain consistent identity across different technical domains (IoT, web, telecom, industrial) with different authentication mechanisms and trust models.",
      "category": "Agent identity/auth",
      "severity": "high",
      "evidence": "152 IETF drafts on agent identity with high overlap suggests fragmented approaches without cross-domain solution",
      "addressed_by": "NIST-AI-Agent-IAM concept paper addresses single-domain identity, IETF drafts focus on specific protocols, but no cross-domain federation standard exists"
    },
    {
      "topic": "AI agent decision audit trails interoperability",
      "description": "Different AI systems use incompatible formats for logging decisions and reasoning processes, making cross-system audit trails impossible. This prevents accountability in multi-agent scenarios where decisions span different platforms and organizations.",
      "category": "Data formats/interop",
      "severity": "high",
      "evidence": "206 IETF drafts on data formats with high overlap indicates lack of consensus on audit trail standardization",
      "addressed_by": "NIST AI RMF mentions documentation requirements, ISO 18829 covers ECM trustworthiness, but no interoperable audit trail format exists"
    },
    {
      "topic": "Adversarial AI agent detection in real-time",
      "description": "Current adversarial ML defenses focus on training-time or static detection, but no standard addresses real-time detection of AI agents that have been compromised or are behaving adversarially during live operations.",
      "category": "AI safety/alignment",
      "severity": "critical",
      "evidence": "NIST AI 100-2 focuses on taxonomy of attacks but not real-time detection, suggesting this operational aspect is underaddressed",
      "addressed_by": "NIST AI 100-2 provides attack taxonomy, ITU-T X.1384 covers security threats, but neither addresses real-time adversarial agent detection"
    },
    {
      "topic": "AI agent capability negotiation protocols",
      "description": "When AI agents from different systems need to collaborate, there's no standard way for them to discover, negotiate, and verify each other's capabilities and limitations. This leads to failed collaborations or dangerous capability assumptions.",
      "category": "A2A protocols",
      "severity": "medium",
      "evidence": "137 IETF drafts on A2A protocols with high overlap suggests multiple competing approaches without standard capability negotiation",
      "addressed_by": "IETF drafts include agent discovery/registration but not capability negotiation, ITU-T Y.4464 covers IoT agent frameworks but not cross-system capability exchange"
    },
    {
      "topic": "Decentralized AI model version control",
      "description": "As AI agents update their models autonomously, there's no decentralized standard for tracking model versions, ensuring compatibility, and preventing conflicts when agents with different model versions interact.",
      "category": "Model serving/inference",
      "severity": "medium",
      "evidence": "Only 67 IETF drafts on model serving vs hundreds on protocols suggests infrastructure gaps in model lifecycle management",
      "addressed_by": "ITU-T Y.3531 covers ML as a service architecture but not decentralized version control, IETF has some federated learning coordination drafts but not version management"
    },
    {
      "topic": "AI agent ethical decision conflict resolution",
      "description": "When AI agents operating under different ethical frameworks need to make joint decisions, there's no standard protocol for resolving ethical conflicts or finding acceptable compromises. This is critical for multi-stakeholder autonomous systems.",
      "category": "AI safety/alignment",
      "severity": "high",
      "evidence": "ITU-T FG-AI4H DEL01 covers ethics for health AI, but no general framework exists for resolving ethical conflicts between agents",
      "addressed_by": "ITU-T FG-AI4H DEL01 addresses AI ethics in health, NIST AI RMF includes governance principles, but no conflict resolution mechanism exists"
    },
    {
      "topic": "Privacy-preserving agent-to-agent communication",
      "description": "While federated learning preserves some privacy, there's no comprehensive standard for AI agents to communicate and collaborate while maintaining privacy of their training data, user interactions, and proprietary algorithms.",
      "category": "A2A protocols",
      "severity": "high",
      "evidence": "ITU-T Y.3178 covers federated learning requirements but broader privacy-preserving communication protocols are missing",
      "addressed_by": "ITU-T Y.3178 addresses federated ML privacy, NIST AI 600-1 covers generative AI privacy, but no comprehensive A2A privacy communication standard"
    },
    {
      "topic": "AI agent behavioral specification languages",
      "description": "There's no standardized formal language for specifying and verifying AI agent behaviors that can be understood across different AI architectures and validated automatically. This prevents reliable behavioral contracts between agents.",
      "category": "Data formats/interop",
      "severity": "medium",
      "evidence": "High overlap in data format drafts suggests lack of consensus on behavioral specification standards",
      "addressed_by": "ISO standards cover individual machine behaviors, W3C has user agent guidelines, but no formal cross-platform behavioral specification language exists"
    }
  ]


