id,system_type,validation_area,check,expected_evidence,metric,threshold_guidance,severity,lifecycle_stage,automation_level,source_type,source_name,source_url,notes
VAL-MODEL-001,model,task_performance,Does the model meet the minimum quality required for its intended task?,"Task-specific evaluation set, baseline model, evaluation report",Primary task metric,Define a use-case-specific minimum and compare against an approved baseline.,high,pre-deployment,automated,framework-inspired,Hugging Face — Choosing a metric,https://huggingface.co/docs/evaluate/en/choosing_a_metric,Practical validation check authored for this dataset; not a verbatim requirement from the cited source.
VAL-MODEL-002,model,robustness,Does model performance remain acceptably stable under realistic prompt or input variation?,"Perturbation suite, repeated runs, robustness report",Performance delta under perturbation,Define the maximum acceptable degradation for representative variations.,high,pre-deployment,automated,framework-inspired,NIST AI Risk Management Framework (AI RMF 1.0),https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-ai-rmf-10,Practical validation check authored for this dataset; not a verbatim requirement from the cited source.
VAL-MODEL-003,model,calibration,Are confidence signals or uncertainty indicators meaningfully aligned with observed correctness where they are used?,"Calibration dataset, confidence outputs, reliability analysis",Calibration error / selective accuracy,Set a calibration target appropriate to the decision context; do not use confidence without validation.,medium,pre-deployment,automated,framework-inspired,NIST AI Risk Management Framework (AI RMF 1.0),https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-ai-rmf-10,Practical validation check authored for this dataset; not a verbatim requirement from the cited source.
VAL-MODEL-004,model,failure_modes,Are known high-impact failure modes documented and reproducible in testing?,"Failure catalog, test prompts, incident examples, model card",Failure reproduction coverage,All identified high-impact failure modes should have at least one reproducible test.,high,pre-deployment,semi-automated,framework-inspired,NIST AI RMF: Generative AI Profile (NIST AI 600-1),https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-generative-artificial-intelligence,Practical validation check authored for this dataset; not a verbatim requirement from the cited source.
VAL-MODEL-005,model,bias_fairness,"Has performance been evaluated across relevant groups, languages, domains or other meaningful slices?","Slice definitions, evaluation results, disparity analysis",Worst-slice performance / disparity,"Define acceptable disparity based on context, impact and population; document unsupported slices.",high,pre-deployment,automated,framework-inspired,NIST AI Risk Management Framework (AI RMF 1.0),https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-ai-rmf-10,Practical validation check authored for this dataset; not a verbatim requirement from the cited source.
VAL-MODEL-006,model,safety,Does the model resist known harmful or disallowed behavior within the intended deployment context?,"Safety test suite, red-team results, refusal/behavior logs",Safety violation rate,Set scenario-specific limits; critical prohibited behaviors may require zero tolerated occurrences in the test set.,critical,pre-deployment,semi-automated,framework-inspired,NIST AI RMF: Generative AI Profile (NIST AI 600-1),https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-generative-artificial-intelligence,Practical validation check authored for this dataset; not a verbatim requirement from the cited source.
VAL-MODEL-007,model,versioning,"Can every deployed model instance be traced to an exact model, revision and configuration?","Model registry record, commit/revision ID, deployment manifest",Traceability coverage,100% of production deployments should map to an exact approved model revision.,high,deployment,automated,framework-inspired,NIST SP 800-218A — Secure Software Development Practices for Generative AI,https://www.nist.gov/publications/secure-software-development-practices-generative-ai-and-dual-use-foundation-models-ssdf,Practical validation check authored for this dataset; not a verbatim requirement from the cited source.
VAL-MODEL-008,model,regression,Are model updates regression-tested against previously approved capabilities and risks?,"Regression suite, prior baseline, change log",Regression pass rate / metric deltas,No release should proceed with unexplained high-impact regressions.,high,pre-deployment,automated,framework-inspired,NIST AI Risk Management Framework (AI RMF 1.0),https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-ai-rmf-10,Practical validation check authored for this dataset; not a verbatim requirement from the cited source.
VAL-MODEL-009,model,resource_behavior,"Are latency, throughput and memory behavior measured under representative load?","Load-test results, hardware profile, serving logs",p95 latency / throughput / memory,Define operational SLOs for the intended environment and validate under expected concurrency.,medium,pre-deployment,automated,framework-inspired,NIST AI Risk Management Framework (AI RMF 1.0),https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-ai-rmf-10,Practical validation check authored for this dataset; not a verbatim requirement from the cited source.
VAL-MODEL-010,model,documentation,"Are intended use, limitations, evaluation scope and unsupported conditions documented?","Model card, validation report, risk documentation",Documentation completeness,All deployment-critical limitations and validation boundaries should be documented before release.,high,pre-deployment,manual,framework-inspired,NIST AI Risk Management Framework (AI RMF 1.0),https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-ai-rmf-10,Practical validation check authored for this dataset; not a verbatim requirement from the cited source.
VAL-AGENT-001,agent,goal_completion,Does the agent complete representative end-to-end tasks successfully?,"Scenario suite, traces, final outcomes",Task success rate,Set a minimum task success rate per workflow and risk level.,high,pre-deployment,automated,framework-inspired,NIST AI Risk Management Framework (AI RMF 1.0),https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-ai-rmf-10,Practical validation check authored for this dataset; not a verbatim requirement from the cited source.
VAL-AGENT-002,agent,tool_selection,Does the agent select only appropriate and permitted tools for each task?,"Tool-call traces, policy definitions, scenario tests",Tool selection accuracy / policy violation rate,Unauthorized or explicitly disallowed tool selection should be zero in the validation suite.,critical,pre-deployment,automated,framework-inspired,OWASP Top 10 for LLM Applications 2025,https://genai.owasp.org/resource/owasp-top-10-for-llm-applications-2025/,Practical validation check authored for this dataset; not a verbatim requirement from the cited source.
VAL-AGENT-003,agent,permissions,Are agent actions constrained by least-privilege permissions?,"Permission matrix, service-account scopes, execution logs",Unauthorized action rate,Zero successful actions outside the agent's approved permission scope.,critical,pre-deployment,semi-automated,framework-inspired,OWASP Top 10 for LLM Applications 2025,https://genai.owasp.org/resource/owasp-top-10-for-llm-applications-2025/,Practical validation check authored for this dataset; not a verbatim requirement from the cited source.
VAL-AGENT-004,agent,trajectory,"Can important agent decisions, tool calls and observations be reconstructed from traces?","Structured traces, tool logs, timestamps, model/version metadata",Trace completeness,100% of high-impact actions should have reconstructable decision and execution evidence.,high,deployment,automated,framework-inspired,NIST AI Risk Management Framework (AI RMF 1.0),https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-ai-rmf-10,Practical validation check authored for this dataset; not a verbatim requirement from the cited source.
VAL-AGENT-005,agent,recovery,"Does the agent recover safely from tool failures, unavailable dependencies and malformed results?","Fault-injection scenarios, traces, recovery outcomes",Safe recovery rate,Define acceptable recovery behavior; no unsafe fallback actions in critical scenarios.,high,pre-deployment,automated,framework-inspired,NIST AI Risk Management Framework (AI RMF 1.0),https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-ai-rmf-10,Practical validation check authored for this dataset; not a verbatim requirement from the cited source.
VAL-AGENT-006,agent,termination,Does the agent stop when its goal is reached or when configured limits are exceeded?,"Loop tests, max-step settings, execution logs",Unbounded-loop rate / excess-step rate,"Zero unbounded executions; enforce hard runtime, step or cost limits.",high,pre-deployment,automated,framework-inspired,OWASP Top 10 for LLM Applications 2025,https://genai.owasp.org/resource/owasp-top-10-for-llm-applications-2025/,Practical validation check authored for this dataset; not a verbatim requirement from the cited source.
VAL-AGENT-007,agent,human_oversight,Are high-impact actions escalated for human approval where required?,"Approval policy, approval logs, action traces",Approval bypass rate,Zero execution of actions that policy marks as requiring approval without recorded approval.,critical,deployment,automated,framework-inspired,NIST AI Risk Management Framework (AI RMF 1.0),https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-ai-rmf-10,Practical validation check authored for this dataset; not a verbatim requirement from the cited source.
VAL-AGENT-008,agent,prompt_injection,Does the agent resist instructions in untrusted content that attempt to override system or tool-use policies?,"Injection test corpus, retrieval/tool scenarios, traces",Injection success rate,Set a strict threshold based on impact; privileged action compromise should be zero in the test suite.,critical,pre-deployment,semi-automated,framework-inspired,OWASP Top 10 for LLM Applications 2025,https://genai.owasp.org/resource/owasp-top-10-for-llm-applications-2025/,Practical validation check authored for this dataset; not a verbatim requirement from the cited source.
VAL-AGENT-009,agent,cost_control,"Are token, tool, compute and external-service costs bounded and observable?","Budgets, cost telemetry, run logs",Cost per successful task / budget breach rate,Define workflow budgets and require zero uncontrolled budget overruns.,medium,deployment,automated,framework-inspired,OWASP Top 10 for LLM Applications 2025,https://genai.owasp.org/resource/owasp-top-10-for-llm-applications-2025/,Practical validation check authored for this dataset; not a verbatim requirement from the cited source.
VAL-AGENT-010,agent,delegation,"In multi-agent systems, are delegation boundaries and responsibility transfers explicit and traceable?","Agent graph, delegation events, scopes, traces",Delegation trace coverage,All inter-agent delegation affecting high-impact actions should be attributable and reconstructable.,high,pre-deployment,semi-automated,framework-inspired,NIST AI Risk Management Framework (AI RMF 1.0),https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-ai-rmf-10,Practical validation check authored for this dataset; not a verbatim requirement from the cited source.
VAL-RAG-001,rag,retrieval_relevance,Does retrieval return information relevant to the user question or task?,"Query set, retrieved passages, relevance judgments",Recall@k / nDCG / precision@k,Choose a retrieval metric and target based on corpus size and downstream task.,high,pre-deployment,automated,framework-inspired,Hugging Face — Choosing a metric,https://huggingface.co/docs/evaluate/en/choosing_a_metric,Practical validation check authored for this dataset; not a verbatim requirement from the cited source.
VAL-RAG-002,rag,grounding,Are generated factual claims supported by retrieved evidence when grounding is required?,"Answers, retrieved context, claim-evidence labels",Grounding error rate / supported-claim rate,Define a maximum unsupported-claim rate; stricter thresholds for high-impact use cases.,critical,pre-deployment,semi-automated,framework-inspired,NIST AI RMF: Generative AI Profile (NIST AI 600-1),https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-generative-artificial-intelligence,Practical validation check authored for this dataset; not a verbatim requirement from the cited source.
VAL-RAG-003,rag,citation_accuracy,Do citations point to sources that actually support the associated claims?,"Generated citations, source documents, citation labels",Citation precision,Set a minimum citation-support rate; critical claims should require directly supporting evidence.,high,pre-deployment,semi-automated,framework-inspired,NIST AI RMF: Generative AI Profile (NIST AI 600-1),https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-generative-artificial-intelligence,Practical validation check authored for this dataset; not a verbatim requirement from the cited source.
VAL-RAG-004,rag,source_provenance,Can each indexed document be traced to an approved source and version?,"Corpus manifest, source URLs, document hashes, ingestion logs",Provenance coverage,100% of production documents should have source and version metadata.,high,deployment,automated,framework-inspired,NIST AI Risk Management Framework (AI RMF 1.0),https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-ai-rmf-10,Practical validation check authored for this dataset; not a verbatim requirement from the cited source.
VAL-RAG-005,rag,access_control,Does retrieval enforce document-level access restrictions for the requesting user or agent?,"ACLs, test identities, retrieval logs",Unauthorized retrieval rate,Zero retrieval of documents outside the requester's approved access scope.,critical,pre-deployment,automated,framework-inspired,OWASP Top 10 for LLM Applications 2025,https://genai.owasp.org/resource/owasp-top-10-for-llm-applications-2025/,Practical validation check authored for this dataset; not a verbatim requirement from the cited source.
VAL-RAG-006,rag,staleness,Are time-sensitive documents refreshed or retired according to defined freshness rules?,"Document timestamps, refresh policy, index audit",Stale-document rate,Define freshness windows per source class and alert on expired content.,medium,monitoring,automated,framework-inspired,NIST AI Risk Management Framework (AI RMF 1.0),https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-ai-rmf-10,Practical validation check authored for this dataset; not a verbatim requirement from the cited source.
VAL-RAG-007,rag,poisoning,Can malicious or manipulated documents alter retrieval or generation in unsafe ways?,"Poisoned-document test set, ingestion controls, RAG traces",Poisoning attack success rate,High-impact poisoning scenarios should not cause policy-violating actions or unsupported trusted claims.,critical,pre-deployment,semi-automated,framework-inspired,OWASP Top 10 for LLM Applications 2025,https://genai.owasp.org/resource/owasp-top-10-for-llm-applications-2025/,Practical validation check authored for this dataset; not a verbatim requirement from the cited source.
VAL-RAG-008,rag,context_overflow,Does the system preserve important evidence when retrieved context exceeds the model context budget?,"Long-context scenarios, selected chunks, outputs",Critical-evidence retention rate,Define critical-evidence retention expectations for long or crowded contexts.,high,pre-deployment,automated,framework-inspired,NIST AI RMF: Generative AI Profile (NIST AI 600-1),https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-generative-artificial-intelligence,Practical validation check authored for this dataset; not a verbatim requirement from the cited source.
VAL-RAG-009,rag,retrieval_failure,Does the system fail safely when no relevant evidence is available?,"No-answer scenarios, outputs, fallback logs",Appropriate abstention rate,Require abstention or explicit uncertainty where evidence is insufficient for high-impact claims.,high,pre-deployment,automated,framework-inspired,NIST AI RMF: Generative AI Profile (NIST AI 600-1),https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-generative-artificial-intelligence,Practical validation check authored for this dataset; not a verbatim requirement from the cited source.
VAL-RAG-010,rag,chunking_indexing,"Are chunking, embedding and indexing choices validated for the target corpus and query types?","Ablation tests, retrieval benchmarks, index configuration",Retrieval quality delta,Compare configurations on representative queries; document chosen trade-offs.,medium,pre-deployment,automated,framework-inspired,Hugging Face Evaluate,https://huggingface.co/docs/evaluate/index,Practical validation check authored for this dataset; not a verbatim requirement from the cited source.
VAL-TOOL-001,tool,schema_validation,Are tool arguments validated against an explicit schema before execution?,"Tool schema, invalid-input tests, execution logs",Invalid-call rejection rate,100% of schema-invalid privileged calls should be rejected before execution.,critical,pre-deployment,automated,framework-inspired,OWASP Top 10 for LLM Applications 2025,https://genai.owasp.org/resource/owasp-top-10-for-llm-applications-2025/,Practical validation check authored for this dataset; not a verbatim requirement from the cited source.
VAL-TOOL-002,tool,output_validation,Are tool outputs treated as untrusted input and validated before downstream use?,"Output validation rules, malformed-output tests, traces",Unsafe output propagation rate,Zero propagation of known-invalid structured outputs into privileged actions.,critical,pre-deployment,automated,framework-inspired,OWASP Top 10 for LLM Applications 2025,https://genai.owasp.org/resource/owasp-top-10-for-llm-applications-2025/,Practical validation check authored for this dataset; not a verbatim requirement from the cited source.
VAL-TOOL-003,tool,side_effects,Are side-effecting tools clearly distinguished from read-only tools?,"Tool registry, capability labels, permission rules",Capability labeling coverage,100% of production tools should declare whether they can create external side effects.,high,design,manual,framework-inspired,NIST AI Risk Management Framework (AI RMF 1.0),https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-ai-rmf-10,Practical validation check authored for this dataset; not a verbatim requirement from the cited source.
VAL-TOOL-004,tool,idempotency,Are retry behaviors safe for tools that may create duplicate or irreversible effects?,"Retry policy, idempotency keys, fault-injection tests",Duplicate side-effect rate,Zero duplicate critical transactions in retry/failure scenarios.,high,pre-deployment,automated,framework-inspired,NIST AI Risk Management Framework (AI RMF 1.0),https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-ai-rmf-10,Practical validation check authored for this dataset; not a verbatim requirement from the cited source.
VAL-TOOL-005,tool,authentication,"Are tool credentials and service identities scoped, rotated and protected?","Credential inventory, secret-management config, access logs",Credential policy coverage,100% of production tool integrations should use approved credential management.,critical,deployment,semi-automated,framework-inspired,NIST SP 800-218A — Secure Software Development Practices for Generative AI,https://www.nist.gov/publications/secure-software-development-practices-generative-ai-and-dual-use-foundation-models-ssdf,Practical validation check authored for this dataset; not a verbatim requirement from the cited source.
VAL-TOOL-006,tool,rate_limits,Are execution frequency and resource limits defined for costly or sensitive tools?,"Rate-limit config, stress tests, alerts",Rate-limit enforcement rate,Zero successful bypasses of configured hard limits in the validation suite.,high,pre-deployment,automated,framework-inspired,OWASP Top 10 for LLM Applications 2025,https://genai.owasp.org/resource/owasp-top-10-for-llm-applications-2025/,Practical validation check authored for this dataset; not a verbatim requirement from the cited source.
VAL-TOOL-007,tool,error_handling,Are tool errors explicit enough for the agent to avoid unsafe assumptions?,"Error taxonomy, simulated failures, traces",Unsafe continuation after error,"High-impact tool failures should trigger safe fallback, stop or escalation behavior.",high,pre-deployment,automated,framework-inspired,NIST AI Risk Management Framework (AI RMF 1.0),https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-ai-rmf-10,Practical validation check authored for this dataset; not a verbatim requirement from the cited source.
VAL-TOOL-008,tool,auditability,"Are tool calls logged with identity, arguments, result status and timestamp?","Execution logs, trace IDs, audit schema",Audit log completeness,100% of high-impact tool executions should have complete audit records.,high,deployment,automated,framework-inspired,NIST AI Risk Management Framework (AI RMF 1.0),https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-ai-rmf-10,Practical validation check authored for this dataset; not a verbatim requirement from the cited source.
VAL-DATA-001,data,provenance,"Can training, fine-tuning and evaluation data be traced to known sources and processing steps?","Dataset card, manifests, lineage records, processing logs",Provenance coverage,Define required lineage fields and target complete coverage for production-critical datasets.,high,design,semi-automated,framework-inspired,NIST AI Risk Management Framework (AI RMF 1.0),https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-ai-rmf-10,Practical validation check authored for this dataset; not a verbatim requirement from the cited source.
VAL-DATA-002,data,quality,"Have missing values, duplicates, corrupt records and label errors been measured?","Data-quality report, validation scripts, sample review",Error rate by defect type,Set dataset-specific quality thresholds and investigate outliers before use.,high,pre-deployment,automated,framework-inspired,Hugging Face Evaluate,https://huggingface.co/docs/evaluate/index,Practical validation check authored for this dataset; not a verbatim requirement from the cited source.
VAL-DATA-003,data,representativeness,"Does the dataset cover the populations, domains and conditions relevant to intended use?","Coverage analysis, slice statistics, domain comparison",Coverage by target slice,Document known gaps and avoid claims for materially underrepresented conditions.,high,pre-deployment,semi-automated,framework-inspired,NIST AI Risk Management Framework (AI RMF 1.0),https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-ai-rmf-10,Practical validation check authored for this dataset; not a verbatim requirement from the cited source.
VAL-DATA-004,data,leakage,Has overlap between training data and evaluation/test data been checked where it could bias results?,"Deduplication/overlap analysis, split manifest",Train-test overlap rate,Define a maximum allowed overlap; benchmark contamination should be investigated and reported.,high,pre-deployment,automated,framework-inspired,NIST AI RMF: Generative AI Profile (NIST AI 600-1),https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-generative-artificial-intelligence,Practical validation check authored for this dataset; not a verbatim requirement from the cited source.
VAL-DATA-005,data,sensitive_information,Is sensitive or restricted information identified and handled according to policy?,"Data classification, DLP scan, access controls",Unapproved sensitive-record rate,Zero knowingly unapproved sensitive records in datasets not authorized to contain them.,critical,pre-deployment,semi-automated,framework-inspired,OWASP Top 10 for LLM Applications 2025,https://genai.owasp.org/resource/owasp-top-10-for-llm-applications-2025/,Practical validation check authored for this dataset; not a verbatim requirement from the cited source.
VAL-DATA-006,data,poisoning_integrity,Are ingestion and update pipelines protected against unauthorized or malicious data changes?,"Access controls, hashes, provenance, anomaly checks",Unauthorized modification rate,Zero unauthorized modifications to approved production data sources.,critical,deployment,semi-automated,framework-inspired,OWASP Top 10 for LLM Applications 2025,https://genai.owasp.org/resource/owasp-top-10-for-llm-applications-2025/,Practical validation check authored for this dataset; not a verbatim requirement from the cited source.
VAL-DATA-007,data,label_consistency,"Where labels or human judgments are used, has agreement and consistency been measured?","Annotation guidelines, multiple annotations, adjudication logs",Inter-annotator agreement,Set an agreement target appropriate to the task and document subjective/ambiguous categories.,medium,pre-deployment,automated,framework-inspired,Hugging Face — Choosing a metric,https://huggingface.co/docs/evaluate/en/choosing_a_metric,Practical validation check authored for this dataset; not a verbatim requirement from the cited source.
VAL-DATA-008,data,drift,Are changes in production input distributions monitored against the validated data profile?,"Reference distribution, production telemetry, drift report",Population/data drift metric,Define alert thresholds by feature or embedding distribution and revalidate after material drift.,high,monitoring,automated,framework-inspired,NIST AI Risk Management Framework (AI RMF 1.0),https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-ai-rmf-10,Practical validation check authored for this dataset; not a verbatim requirement from the cited source.
VAL-MM-001,multimodal,cross_modal_consistency,"Do text, image, audio or video outputs remain consistent when modalities describe the same underlying fact?","Paired multimodal test set, consistency labels",Cross-modal consistency rate,Set a minimum consistency target on representative paired examples.,high,pre-deployment,semi-automated,framework-inspired,NIST AI RMF: Generative AI Profile (NIST AI 600-1),https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-generative-artificial-intelligence,Practical validation check authored for this dataset; not a verbatim requirement from the cited source.
VAL-MM-002,multimodal,image_grounding,"When answering about an image, are claims supported by visible content rather than unsupported assumptions?","Images, questions, region/claim annotations, outputs",Visual grounding error rate,Define a maximum unsupported visual-claim rate for the intended task.,high,pre-deployment,semi-automated,framework-inspired,NIST AI RMF: Generative AI Profile (NIST AI 600-1),https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-generative-artificial-intelligence,Practical validation check authored for this dataset; not a verbatim requirement from the cited source.
VAL-MM-003,multimodal,audio_transcription,"If speech recognition is used, is transcription quality measured across relevant accents, noise and domains?","Audio benchmark, transcripts, slice labels",WER / slice WER,Set use-case-specific WER targets and examine worst-performing slices.,high,pre-deployment,automated,framework-inspired,Hugging Face — Choosing a metric,https://huggingface.co/docs/evaluate/en/choosing_a_metric,Practical validation check authored for this dataset; not a verbatim requirement from the cited source.
VAL-MM-004,multimodal,modality_injection,"Can malicious instructions embedded in images, documents or audio override system policies?","Adversarial multimodal inputs, traces, outputs",Cross-modal injection success rate,Privileged policy compromise should be zero in the validation suite.,critical,pre-deployment,semi-automated,framework-inspired,OWASP Top 10 for LLM Applications 2025,https://genai.owasp.org/resource/owasp-top-10-for-llm-applications-2025/,Practical validation check authored for this dataset; not a verbatim requirement from the cited source.
VAL-MM-005,multimodal,missing_modality,"Does the system fail safely when a required modality is missing, unreadable or corrupted?","Corrupted/missing input scenarios, outputs",Safe failure rate,"All required-modality failures should produce explicit error, abstention or fallback behavior.",high,pre-deployment,automated,framework-inspired,NIST AI Risk Management Framework (AI RMF 1.0),https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-ai-rmf-10,Practical validation check authored for this dataset; not a verbatim requirement from the cited source.
VAL-MM-006,multimodal,format_robustness,"Is behavior stable across common encoding, resolution, compression or sampling variations?","Perturbed media set, outputs",Performance delta by transformation,Define acceptable degradation for supported media transformations.,medium,pre-deployment,automated,framework-inspired,NIST AI Risk Management Framework (AI RMF 1.0),https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-ai-rmf-10,Practical validation check authored for this dataset; not a verbatim requirement from the cited source.
VAL-MM-007,multimodal,synthetic_content,Are workflows that depend on content authenticity explicit about limitations of detecting or interpreting synthetic media?,"Product documentation, provenance signals, evaluation set",Detection/attribution performance,Do not make unsupported authenticity guarantees; validate claims on representative data.,high,pre-deployment,semi-automated,framework-inspired,NIST AI RMF: Generative AI Profile (NIST AI 600-1),https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-generative-artificial-intelligence,Practical validation check authored for this dataset; not a verbatim requirement from the cited source.
VAL-MM-008,multimodal,privacy,"Are faces, voices, documents or other sensitive media handled according to access and retention policy?","Data-flow map, retention policy, access logs",Policy violation rate,Zero processing or retention outside approved policy for sensitive multimodal data.,critical,deployment,semi-automated,framework-inspired,NIST AI Risk Management Framework (AI RMF 1.0),https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-ai-rmf-10,Practical validation check authored for this dataset; not a verbatim requirement from the cited source.
VAL-SYS-001,system,intended_use,"Is the intended use, user population and deployment context explicitly defined?","System card, requirements, use-case documentation",Scope completeness,Define intended use and material out-of-scope uses before validation begins.,high,design,manual,framework-inspired,NIST AI Risk Management Framework (AI RMF 1.0),https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-ai-rmf-10,Practical validation check authored for this dataset; not a verbatim requirement from the cited source.
VAL-SYS-002,system,risk_ownership,"Are owners assigned for model, data, security, validation and operational risks?","RACI/ownership matrix, governance records",Ownership coverage,Every high-impact risk and control should have a named accountable owner.,high,design,manual,framework-inspired,ISO/IEC 42001:2023 — AI management systems,https://www.iso.org/standard/42001,Practical validation check authored for this dataset; not a verbatim requirement from the cited source.
VAL-SYS-003,system,change_management,"Do material changes to models, prompts, tools, data or policies trigger revalidation?","Change policy, release records, revalidation logs",Revalidation trigger coverage,100% of predefined material changes should trigger the required revalidation workflow.,high,deployment,semi-automated,framework-inspired,ISO/IEC 42001:2023 — AI management systems,https://www.iso.org/standard/42001,Practical validation check authored for this dataset; not a verbatim requirement from the cited source.
VAL-SYS-004,system,incident_response,"Can AI incidents be detected, triaged, investigated and linked to model/system evidence?","Incident playbook, logging, drills, postmortems",Detection-to-triage time / evidence completeness,Define incident SLOs and require complete evidence for high-impact events.,critical,deployment,semi-automated,framework-inspired,NIST AI Risk Management Framework (AI RMF 1.0),https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-ai-rmf-10,Practical validation check authored for this dataset; not a verbatim requirement from the cited source.
VAL-SYS-005,system,monitoring,Are production metrics linked to the assumptions and thresholds used during validation?,"Validation report, dashboards, alert rules",Monitoring coverage,Monitor every production-critical validation criterion that can materially drift over time.,high,monitoring,automated,framework-inspired,NIST AI Risk Management Framework (AI RMF 1.0),https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-ai-rmf-10,Practical validation check authored for this dataset; not a verbatim requirement from the cited source.
VAL-SYS-006,system,validation_documentation,"Is there a reproducible validation record covering scope, methods, datasets, metrics, results, limitations and approval?","Validation report, test artifacts, sign-off record",Documentation completeness,No production approval without a traceable validation record and documented residual risks.,high,pre-deployment,manual,framework-inspired,Hugging Face Evaluate,https://huggingface.co/docs/evaluate/index,Practical validation check authored for this dataset; not a verbatim requirement from the cited source.