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

Project description

CI Release PyPI Docs Python License

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] [--push] [--dry-run] [--idempotency-path PATH] [--disable-idempotency]
  • ai-act export history <event_id> --target jira|servicenow|generic [--output PATH] [--history-path PATH] [--json] [--push] [--dry-run] [--idempotency-path PATH] [--disable-idempotency]

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:

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

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)
  • Phase 15: CI/release runtime hardening completed (Node20 deprecation cleanup + security gate stabilization)
  • Phase 16: live export push completed (strict fail-fast + retry/backoff controls for --push)
  • Phase 17: export push production hardening in progress (create-only idempotency ledger + duplicate-safe push)

Disclaimer

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

License

Apache License 2.0. See LICENSE.

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