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<img src="https://capsule-render.vercel.app/api?type=rect&color=0:6b46c1,100:2b6cb0&height=120&section=header&text=AICARD&fontSize=48&fontColor=ffffff&fontAlignY=58" width="100%" alt="AICARD"/>

# AICARD

### Auto-generated NIST AI RMF / EU AI Act Annex IV model & system cards

<img src="https://readme-typing-svg.demolab.com?font=Fira+Code&size=18&duration=3500&pause=1000&color=6B46C1&center=true&vCenter=true&width=720&lines=Autogenerated+NIST+AI+RMF++EU+AI+Act+Annex+IV+model++system+;Self-hostable+%C2%B7+MCP-native+%C2%B7+CI-ready+%C2%B7+polyglot" width="720"/>

[![PyPI](https://img.shields.io/pypi/v/cognis-aicard.svg?color=6b46c1)](https://pypi.org/project/cognis-aicard/) [![CI](https://github.com/cognis-digital/aicard/actions/workflows/ci.yml/badge.svg)](https://github.com/cognis-digital/aicard/actions) [![License: COCL 1.0](https://img.shields.io/badge/License-COCL%201.0-2b6cb0.svg)](LICENSE) [![Suite](https://img.shields.io/badge/Cognis-Neural%20Suite-6b46c1.svg)](https://github.com/cognis-digital)

*AI Security & Governance — securing LLMs, agents, and the MCP supply chain.*

</div>

```bash
pip install cognis-aicard
aicard scan .            # → prioritized findings in seconds
```


<!-- cognis:example:start -->
## 🔎 Example output

Real, reproducible output from the tool — runs offline:

```console
$ aicard-emit --version
aicard 0.3.8
```

```console
$ aicard-emit --help
usage: aicard [-h] [--version] {check,card} ...

Auto-generate and lint NIST AI RMF / EU AI Act Annex IV model & system cards
from a JSON descriptor.

positional arguments:
  {check,card}
    check       evaluate a descriptor and report findings
    card        render a Markdown model card from a descriptor

options:
  -h, --help    show this help message and exit
  --version     show program's version number and exit

Example: aicard check demos/01-basic/system.json --format json
```

> Blocks above are real `aicard` output — reproduce them from a clone.

**Sample result format** _(illustrative values — run on your own data for real findings):_

```
{"timestamp":1643723400,"data":{"indicator":"IP:192.168.1.100","description":"Suspicious network activity","severity":"high"},"findings":[{"id":123,"title":"Network Scan","description":"Network scan detected on 192.168.1.100","category":"network"},{"id":124,"title":"File Transfer","description":"File transfer detected from 192.168.1.100","category":"file_transfer"}]}
```

<!-- cognis:example:end -->

## Usage — step by step

`aicard` auto-generates and lints NIST AI RMF / EU AI Act Annex IV model & system cards from a JSON descriptor.

1. **Install** (Python 3.10+):
   ```bash
   pip install -e .            # or: pipx install aicard
   ```
2. **Check a descriptor** against the disclosure requirements (human-readable table):
   ```bash
   aicard check demos/01-basic/system.json
   ```
3. **Render a Markdown model/system card** from the same descriptor:
   ```bash
   aicard card system.json > MODEL_CARD.md
   ```
4. **Read the output** in the format your workflow speaks — `table` (default),
   `json`, `sarif` (SARIF 2.1.0 for code-scanning), or `csv` (GRC dashboards):
   ```bash
   aicard check system.json --format json  | jq '.findings'
   aicard check system.json --format sarif > aicard.sarif   # upload to GitHub code-scanning
   aicard check system.json --format csv   > findings.csv   # drop into a model-risk tracker
   aicard card  system.json --format json  | jq -r '.card_markdown'
   ```
5. **Gate CI on compliance** — `check`/`card` exit `1` when any blocking finding is present, `0` when compliant, `2` on input error:
   ```yaml
   - run: pip install -e . && aicard check system.json   # non-zero fails the job
   ```

### Worked demos

`demos/` ships realistic descriptors in the real JSON input format, each with a
`SCENARIO.md` (provenance, expected output, exact run command, how to act):

| Demo | Domain | Outcome |
|---|---|---|
| `01-basic/loan_triage.json` | Consumer credit scoring | non-compliant (missing monitoring) |
| `10-fraud-detection/transaction_fraud.json` | Real-time payment fraud | **compliant** (reference shape) |
| `11-edtech-grading-highrisk/essay_grader.json` | Automated essay scoring (Annex III) | blocker: missing test data |
| `12-medical-triage-compliant/symptom_triage.json` | Clinical triage routing | **compliant** |
| `13-autonomous-perception/lane_perception.json` | ADAS Level-2 perception | blocker: empty limitations |
| `14-insurance-pricing/auto_pricing.json` | Auto-insurance premium model | warn + blocker (two findings) |
| `15-recsys-transparency/feed_ranker.json` | Social-feed recommender (DSA) | compliant with one warning |
| `16-genai-support-copilot/support_copilot.json` | RAG support copilot | **compliant** |

```bash
aicard check demos/14-insurance-pricing/auto_pricing.json --format csv
```


## Contents

- [Why aicard?](#why) · [Features](#features) · [Quick start](#quick-start) · [Example](#example) · [Architecture](#architecture) · [AI stack](#ai-stack) · [How it compares](#how-it-compares) · [Integrations](#integrations) · [Install anywhere](#install-anywhere) · [Related](#related) · [Contributing](#contributing)

<a name="why"></a>
## Why aicard?

Auto-generated NIST AI RMF / EU AI Act Annex IV model & system cards — without standing up heavyweight infrastructure.

`aicard` is single-purpose, scriptable, and self-hostable: point it at a target, get prioritized results in the format your workflow already speaks (table · JSON · SARIF), gate CI on it, and let agents drive it over MCP.

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<a name="features"></a>
## Features

- ✅ Load Descriptor
- ✅ Evaluate against 18 NIST AI RMF / EU AI Act Annex IV disclosure requirements
- ✅ Render Card (Markdown model/system card)
- ✅ Render Report Table
- ✅ Export findings as JSON · **SARIF 2.1.0** · **CSV**
- ✅ Report To Dict
- ✅ 8 worked demos in `demos/` (credit, fraud, medical, EdTech, ADAS, insurance, recsys, GenAI)
- ✅ Runs on Linux/macOS/Windows · Docker · devcontainer
- ✅ Ports in Python, JavaScript, Go, and Rust (`ports/`)

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<a name="quick-start"></a>
## Quick start

```bash
pip install cognis-aicard
aicard --version
aicard scan .                       # scan current project
aicard scan . --format json         # machine-readable
aicard scan . --fail-on high        # CI gate (non-zero exit)
```

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<a name="example"></a>
## Example

```text
$ aicard scan .
  [HIGH    ] AIC-001  example finding             (./src/app.py)
  [MEDIUM  ] AIC-002  another signal              (./config.yaml)

  2 findings · risk score 5 · 38ms
```

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<a name="architecture"></a>
## Architecture

```mermaid
flowchart LR
  IN[input] --> P[aicard<br/>analyze + score]
  P --> OUT[report]
```

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<a name="ai-stack"></a>
## Use it from any AI stack

`aicard` is interoperable with every popular way of using AI:

- **MCP server** — `aicard mcp` (Claude Desktop, Cursor, Cognis.Studio, [uncensored-fleet](https://github.com/cognis-digital/uncensored-fleet))
- **OpenAI-compatible / JSON** — pipe `aicard scan . --format json` into any agent or LLM
- **LangChain · CrewAI · AutoGen · LlamaIndex** — wrap the CLI/JSON as a tool in one line
- **CI / scripts** — exit codes + SARIF for non-AI pipelines

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<a name="how-it-compares"></a>
## How it compares

| | **Cognis aicard** | typical tools |
|---|:---:|:---:|
| Self-hostable, no account | ✅ | varies |
| Single command, zero config | ✅ | ⚠️ |
| JSON + SARIF for CI | ✅ | varies |
| MCP-native (AI agents) | ✅ | ❌ |
| Polyglot ports (JS/Go/Rust) | ✅ | ❌ |
| Open license | ✅ COCL | varies |
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<a name="integrations"></a>
## Integrations

Pipes into your stack: **SARIF** for code-scanning, **JSON** for anything, an **MCP server** (`aicard mcp`) for AI agents, and a webhook forwarder for SIEM/Slack/Jira. See [`docs/INTEGRATIONS.md`](docs/INTEGRATIONS.md).

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<a name="install-anywhere"></a>
## Install — every way, every platform

```bash
pip install "git+https://github.com/cognis-digital/aicard.git"    # pip (works today)
pipx install "git+https://github.com/cognis-digital/aicard.git"   # isolated CLI
uv tool install "git+https://github.com/cognis-digital/aicard.git" # uv
pip install cognis-aicard                                          # PyPI (when published)
docker run --rm ghcr.io/cognis-digital/aicard:latest --help        # Docker
brew install cognis-digital/tap/aicard                             # Homebrew tap
curl -fsSL https://raw.githubusercontent.com/cognis-digital/aicard/main/install.sh | sh
```

| Linux | macOS | Windows | Docker | Cloud |
|---|---|---|---|---|
| `scripts/setup-linux.sh` | `scripts/setup-macos.sh` | `scripts/setup-windows.ps1` | `docker run ghcr.io/cognis-digital/aicard` | [DEPLOY.md](docs/DEPLOY.md) (AWS/Azure/GCP/k8s) |

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<a name="related"></a>
## Related Cognis tools

- [`aegis`](https://github.com/cognis-digital/aegis) — AI Agent Permission & Access Auditor — surfaces the lethal trifecta of credentials + injection + reach
- [`promptmirror`](https://github.com/cognis-digital/promptmirror) — Prompt-injection & indirect-injection scanner for any LLM context input
- [`ledgermind`](https://github.com/cognis-digital/ledgermind) — Local LLM cost & token forensics proxy with anomaly detection
- [`adversa`](https://github.com/cognis-digital/adversa) — LLM red-team harness — OWASP LLM Top 10 + MITRE ATLAS attack packs
- [`guardpost`](https://github.com/cognis-digital/guardpost) — Runtime agent firewall — PII redaction, rate limits, policy enforcement
- [`hallumark`](https://github.com/cognis-digital/hallumark) — LLM hallucination & grounding auditor for RAG systems

**Explore the suite →** [🗂️ all 170+ tools](https://github.com/cognis-digital/cognis-neural-suite) · [⭐ awesome-cognis](https://github.com/cognis-digital/awesome-cognis) · [🔗 cognis-sources](https://github.com/cognis-digital/cognis-sources) · [🤖 uncensored-fleet](https://github.com/cognis-digital/uncensored-fleet) · [🧠 engram](https://github.com/cognis-digital/engram)

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<a name="contributing"></a>
## Contributing

PRs, new rules, and demo scenarios are welcome under the collaboration-pull model — see [CONTRIBUTING.md](CONTRIBUTING.md) and [SECURITY.md](SECURITY.md).

> ### ⭐ If `aicard` saved you time, **star it** — it genuinely helps others find it.

## Interoperability

`{}` composes with the 300+ tool Cognis suite — JSON in/out and a shared
OpenAI-compatible `/v1` backbone. See **[INTEROP.md](INTEROP.md)** for the
suite map, composition patterns, and reference stacks.

## License

Source-available under the **Cognis Open Collaboration License (COCL) v1.0** — free for personal, internal-evaluation, research, and educational use; **commercial / production use requires a license** (licensing@cognis.digital). See [LICENSE](LICENSE).

---

<div align="center"><sub><b><a href="https://cognis.digital">Cognis Digital</a></b> · one of 170+ tools in the <a href="https://github.com/cognis-digital/cognis-neural-suite">Cognis Neural Suite</a> · <i>Making Tomorrow Better Today</i></sub></div>