eu-ai-act-compliance-kit 0.1.2

Automated EU AI Act (2024/1689) compliance checker. Classifies AI systems by risk tier, generates checklists, and produces audit-ready reports.

EU AI Act Compliance Kit

Open-source toolkit to operationalize EU AI Act (Regulation 2024/1689) obligations.
It classifies AI systems by risk tier, evaluates compliance evidence, generates actionable checklists, and produces audit-ready reports.

Why This Exists

Teams building AI for EU markets need a practical path from policy text to engineering controls. This project provides that path:

• Risk classification (unacceptable, high_risk, limited, minimal)
• Evidence-based compliance checks (status model: compliant, partial, non_compliant, not_assessed)
• Checklist and remediation workflow tied to article-level obligations
• Auditable reporting in json, md, html, pdf
• CI/CD + pre-push gates aligned with deterministic fail policy
• History and dashboard artifacts for trend visibility across systems

End-to-End Pipeline

flowchart LR
    A["AI System Descriptor (YAML)"] --> B["validate"]
    B --> C["classify --json"]
    C --> D["check --json"]
    D --> E["checklist"]
    D --> F["report (json|md|html|pdf)"]
    D --> G["history append (JSONL)"]
    C --> H["articles"]
    D --> I["dashboard build"]
    G --> I

CI/CD and Action Gate Flow

flowchart LR
    A["PR / Push"] --> B["GitHub Action: classify + check + report"]
    B --> C{"risk_tier == unacceptable?"}
    C -- "yes" --> Z["Fail"]
    C -- "no" --> D{"risk_tier == high_risk\nAND non_compliant_count > 0\nAND fail_on_high_risk=true?"}
    D -- "yes" --> Z
    D -- "no" --> E["Pass"]
    B --> F["Outputs: compliance %, counts, report path"]

Quick Start

Install

pip install eu-ai-act-compliance-kit
# or
pip install -e .

For PDF export support:

pip install -e ".[reporting]"

Run

ai-act validate examples/medical_diagnosis.yaml
ai-act classify examples/medical_diagnosis.yaml --json
ai-act check examples/medical_diagnosis.yaml --json
ai-act checklist examples/medical_diagnosis.yaml --format md -o checklist.md
ai-act report examples/medical_diagnosis.yaml --format html -o report.html
ai-act export check examples/medical_diagnosis.yaml --target generic --json

CLI Surface

• ai-act classify <system.yaml> [--json]
• ai-act check <system.yaml> [--json]
• ai-act checklist <system.yaml> [--format json|md|html]
• ai-act transparency <system.yaml> [--json]
• ai-act gpai <model.yaml> [--json]
• ai-act report <system.yaml> [--format json|md|html|pdf]
• ai-act validate <system.yaml>
• ai-act articles [--tier minimal|limited|high_risk|unacceptable]
• ai-act history list|show|diff
• ai-act dashboard build <descriptor_dir> [--recursive] [--include-history]
• ai-act export check <system.yaml> --target jira|servicenow|generic [--output PATH] [--history-path PATH] [--json]
• ai-act export history <event_id> --target jira|servicenow|generic [--output PATH] [--history-path PATH] [--json]

Full reference: docs/cli-reference.md

Example Systems

• examples/medical_diagnosis.yaml (high risk)
• examples/hiring_tool.yaml (high risk)
• examples/social_scoring.yaml (unacceptable)
• examples/chatbot.yaml (minimal)
• examples/spam_filter.yaml (minimal)
• examples/gpai_model.yaml / examples/gpai_model_low_risk.yaml

GitHub Action Contract

Action entrypoint: action.yml

Outputs:

• risk_tier
• compliance_percentage
• report_path
• articles_applicable
• total_requirements
• compliant_count
• non_compliant_count
• partial_count
• not_assessed_count

Fail policy:

• unacceptable always fails
• high_risk fails only when fail_on_high_risk=true and non_compliant_count > 0

For UK Global Talent Evidence

This repository is structured to generate verifiable signals of technical impact:

• Measurable output artifacts: compliance reports, checklist items, history events, static dashboards
• Release discipline: semver tag-driven pipeline (qa-build -> trusted PyPI publish -> GitHub Release)
• Open contribution readiness: CI, tests, docs, contribution guide, roadmap, changelog
• Public traceability: issues, PRs, release notes, and workflow history

Evidence-friendly links:

• Repo: https://github.com/ogulcanaydogan/eu-ai-act-compliance-kit
• Docs: https://eu-ai-act-compliance-kit.readthedocs.io
• Launch Evidence: docs/launch_evidence_v0_1_0.md
• Roadmap: ROADMAP.md
• Changelog: CHANGELOG.md
• Contributing: CONTRIBUTING.md

Open-Core Boundary (Commercial Strategy)

Open-source scope (Apache-2.0)

• Core compliance engine (classification/checker/checklist/transparency/gpai)
• CLI + report generation + local history/dashboard
• Documentation, examples, and CI integration

Reserved commercial scope (private)

• Enterprise policy packs and jurisdiction overlays
• Managed multi-tenant dashboard / hosted compliance ops
• Advisory automation and premium support SLAs
• Proprietary integrations and deployment controls

Development

pip install -e ".[dev,docs]"
pytest -q
mkdocs build --strict

First Contribution Path

pip install -e ".[dev,docs]"
./scripts/quickstart_smoke.sh
pre-commit install --hook-type pre-push
pre-commit run --hook-stage pre-push --all-files

If all checks pass, pick a small docs or test issue, open a focused PR, and include command outputs in the PR description.

Local pre-push gate:

pre-commit install --hook-type pre-push
pre-commit run --hook-stage pre-push --all-files

Documentation

• Documentation Home
• Installation
• Quickstart
• CLI Reference
• API Reference
• Custom Systems
• Examples
• Adoption Evidence Template

Roadmap Status

• Phase 1-12: completed (including v0.1.0 launch closure)
• Phase 13: adoption hardening completed
• Phase 14: external export core completed (payload-first, no live API push)

Disclaimer

This project provides technical compliance signals and engineering guidance. It is not legal advice.

License

Apache License 2.0. See LICENSE.