validation/ai-validation-checklists

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

Schema

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.