README.md

---
pretty_name: AI Validation Checklists
language:
- en
tags:
- ai
- validation
- evaluation
- agents
- rag
- multimodal
- safety
- reliability
- governance
task_categories:
- text-classification
---

# 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

```json
{
  "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:

```text
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:

```text
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.**