Datasets:

validation
/

ai-validation-checklists

id stringlengths 10 13	system_type stringclasses 7 values	validation_area stringlengths 5 24	check stringlengths 61 122	expected_evidence stringlengths 28 64	metric stringlengths 15 48	threshold_guidance stringlengths 60 115	severity stringclasses 3 values	lifecycle_stage stringclasses 4 values	automation_level stringclasses 3 values	source_type stringclasses 1 value	source_name stringclasses 7 values	source_url stringclasses 7 values	notes stringclasses 1 value
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.

AI Validation Checklists

AI Validation Checklists is a structured reference dataset for validating AI systems across multiple system types and lifecycle stages.

It is designed for practical use in:

model validation,
agent validation,
RAG validation,
tool-use validation,
data validation,
multimodal validation,
system-level validation and governance.

The dataset currently contains 60 practical validation checks.

Important: These checks are an independent, practical framework authored for this dataset. They are not verbatim requirements, certifications, legal advice, or an official checklist from NIST, ISO, OWASP, Hugging Face, or any other cited organization.

System types

System type	Checks
Model	10
Agent	10
RAG	10
Tool	8
Data	8
Multimodal	8
System	6

Schema

Field	Description
`id`	Stable validation-check identifier
`system_type`	Model, agent, RAG, tool, data, multimodal, or system
`validation_area`	Validation topic such as robustness, grounding, permissions, provenance, or monitoring
`check`	Concrete validation question
`expected_evidence`	Evidence that can support the validation activity
`metric`	Example metric or observable
`threshold_guidance`	Practical guidance for defining a project-specific threshold
`severity`	`medium`, `high`, or `critical`
`lifecycle_stage`	Design, pre-deployment, deployment, or monitoring
`automation_level`	Manual, semi-automated, or automated
`source_type`	How the cited source relates to the check
`source_name`	Reference source
`source_url`	Primary/reference URL
`notes`	Scope and interpretation notes

Example

{
  "id": "VAL-AGENT-001",
  "system_type": "agent",
  "validation_area": "goal_completion",
  "check": "Does the agent complete representative end-to-end tasks successfully?",
  "expected_evidence": "Scenario suite, traces, final outcomes",
  "metric": "Task success rate",
  "threshold_guidance": "Set a minimum task success rate per workflow and risk level.",
  "severity": "high",
  "lifecycle_stage": "pre-deployment",
  "automation_level": "automated",
  "source_type": "framework-inspired",
  "source_name": "NIST AI Risk Management Framework (AI RMF 1.0)",
  "source_url": "https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-ai-rmf-10",
  "notes": "Practical validation check authored for this dataset; not a verbatim requirement from the cited source."
}

How to use the dataset

A validation program can filter checks by:

system_type
+
validation_area
+
severity
+
lifecycle_stage
+
automation_level

For example:

an agent team can select all agent checks with high or critical severity;
a RAG team can focus on grounding, citation_accuracy, access_control, and poisoning;
an enterprise validation review can combine model, data, and system checks before deployment;
a monitoring program can select checks whose lifecycle stage is monitoring.

Thresholds are not universal

The threshold_guidance field intentionally avoids pretending that one numerical threshold works for every system.

Validation thresholds should depend on:

intended use,
risk and impact,
user population,
operating environment,
baseline performance,
regulatory or contractual requirements,
business tolerance,
human oversight.

Some controls — such as unauthorized privileged actions — may reasonably require zero tolerated successes in the validation suite, while performance metrics usually require use-case-specific targets.

Source methodology

The dataset is informed by public resources covering AI risk management, evaluation, security, validation, governance, and testing.

Primary references include:

NIST AI Risk Management Framework (AI RMF 1.0)
https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-ai-rmf-10
NIST AI RMF: Generative AI Profile (NIST AI 600-1)
https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-generative-artificial-intelligence
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
OWASP Top 10 for LLM Applications 2025
https://genai.owasp.org/resource/owasp-top-10-for-llm-applications-2025/
ISO/IEC 42001:2023 — AI management systems
https://www.iso.org/standard/42001
Hugging Face Evaluate
https://huggingface.co/docs/evaluate/index

The checklist items are editorial operationalizations inspired by these sources. A citation means the source is relevant context for the check; it does not mean the source uses the same wording or defines the included metric/threshold.

Files

validation_checklists.csv — convenient for the Hugging Face Dataset Viewer and spreadsheet-style inspection
validation_checklists.jsonl — convenient for programmatic use and downstream applications

Suggested subsets for future versions

A future release could split or expose views such as:

models
agents
rag
tools
data
multimodal
enterprise

Additional future fields could include:

control_family
test_method
example_test_case
risk_if_failed
required_role
framework_mapping
last_reviewed

Quality principles

Practical, not performative

Every row should translate into a test, review, evidence request, or operational check.

Evidence-oriented

The dataset asks what evidence should exist, not only whether a system “seems safe.”

System-aware

Models, agents, RAG systems, tools, data pipelines, and multimodal systems have different failure modes.

Lifecycle-aware

Validation is not only a pre-launch activity. Some properties must be monitored and revalidated after deployment.

Framework-informed, framework-independent

The dataset draws on established public resources without claiming to reproduce or replace them.

Contributing

Useful contributions include:

additional validation checks,
improved metrics,
clearer evidence requirements,
new system types,
better test methods,
additional primary sources,
corrections to outdated references.

Please keep proposed checks specific, testable, and source-aware.

Collaboration

Collaboration around AI validation, agent reliability, evaluation, testing, governance, safety and validation infrastructure is welcome.

Contact: agenten@magenta.de

Disclaimer

This dataset is an independent technical resource.

It is not an official NIST, ISO, OWASP, Hugging Face, regulatory, certification, or audit artifact. It does not provide legal, compliance, certification, or professional audit advice.

Organizations should define validation criteria based on their own systems, risks, jurisdictions, contractual obligations and qualified professional guidance.

Validation — evidence before deployment, monitoring after release.

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