riskforge 1.0.0

EU AI Act Article 9 Risk Management System — open-source CLI

RiskForge

EU AI Act Article 9 risk management, as a developer workflow.

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RiskForge is an open-source CLI that turns EU AI Act Article 9 compliance from a consultant invoice into a 30-minute developer workflow.

Answer 37 guided questions across 8 EU AI Act risk dimensions. RiskForge produces a SHA-256-signed Risk Management File (JSON + PDF) suitable for inclusion in your Annex IV technical documentation pack — ready for your legal team and your downstream compliance toolchain. (Not a substitute for notified-body conformity assessment.)

Built by AI Exponent LLC. Apache 2.0. Runs entirely offline after pip install.

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Quick Start

pip install riskforge

# 1. Register your AI system
riskforge init \
  --name "Loan Scoring Model" \
  --sys-version "2.1" \
  --purpose "Automated credit scoring for retail loan applications." \
  --provider "Acme Financial Services" \
  --category essential_services

# 2. Run the guided 8-dimension risk assessment (~30 minutes)
riskforge assess <system-id> \
  --assessor-name "Alice Chen" \
  --assessor-role "AI Governance Lead"

# 3. (Optional) Derive Article 9(6)–(8) test requirements per identified risk
riskforge tests generate <system-id>

# 4. Check completeness before export
riskforge validate <system-id>

# 5. Export your Article 9 Risk Management File
riskforge export <system-id> --format pdf --output rmf.pdf
riskforge export <system-id> --format json --output rmf.json

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Why RiskForge

EU AI Act Article 9 requires providers of high-risk AI systems to maintain a documented risk management system throughout the system's lifecycle.

The current alternatives:

Option	Cost	Time	Repeatable
Big 4 consulting	€80K–€350K per system¹	Weeks	No
Enterprise GRC platforms	$60K–$200K/year¹	Months	Partial
Spreadsheets	Free	Days	No
RiskForge	Free	~30 min	Yes

^{¹ Indicative market figures gathered from public Big-4 governance-engagement quotes and 2024–2026 enterprise GRC pricing pages. Not a benchmark study; your mileage will vary by scope, jurisdiction, and incumbent advisor.}

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Architecture

RiskForge has four strictly-decoupled layers with CI-enforced import boundaries:

graph TD
    CLI["CLI (Typer)<br/>riskforge init / assess / validate / export / verify"]
    Engine["Engine Layer<br/>AuditEngine · RiskEngine · ValidateEngine<br/>AssessEngine · ExportEngine · TestDerivationEngine"]
    Storage["Storage (FileStore)<br/>YAML + JSONL · chmod 600/700 · async · pluggable ABC"]
    Adapters["Integration Adapters<br/>RAGBenchmarkingAdapter · TraceForgeAdapter<br/>Discovered via Python entry_points"]

    CLI -->|"calls engine functions"| Engine
    Engine -->|"reads/writes via StorageBackend ABC"| Storage
    Engine -->|"adapter pattern — no hard imports"| Adapters

    style CLI fill:#1e3a5f,color:#fff
    style Engine fill:#1e3a5f,color:#fff
    style Storage fill:#1e3a5f,color:#fff
    style Adapters fill:#1e3a5f,color:#fff

State on disk:

your-project/
├── riskforge.yaml              ← project manifest
├── .riskforge/
│   ├── audit.jsonl             ← append-only hash-chained audit log
│   └── .nodelete               ← deletion sentinel
└── systems/<system-id>/
    ├── system.yaml
    ├── register.yaml
    └── exports/
        └── rmf-*.json

Plain YAML + JSONL — readable by regulators without RiskForge installed, diff-able in GitHub PRs.

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AI Exponent compliance toolchain — planned integration

RiskForge is designed to integrate with the broader AI Exponent toolchain. Today, only RiskForge and rag-benchmarking are available on PyPI. The other nodes below are on the public roadmap and will integrate via plain JSON files when they ship.

graph LR
    RAG["rag-benchmarking<br/>(accuracy evidence)<br/><i>shipped</i>"]
    TF["TraceForge<br/>(data governance)<br/><i>roadmap</i>"]
    RF["RiskForge<br/>(Art. 9 RMS)<br/><i>shipped</i>"]
    TD["TransparencyDeck<br/>(Art. 13 docs)<br/><i>roadmap</i>"]
    CB["ConformityBot<br/>(Art. 43 cert)<br/><i>roadmap</i>"]
    CCO["Compliance Officer<br/>(PDF)"]

    RAG -->|"benchmark_report.json"| RF
    TF  -.->|"trace_report.json (planned)"| RF
    RF  -->|"rmf.pdf"| CCO
    RF  -.->|"rmf.json (planned)"| TD
    RF  -.->|"rmf.json (planned)"| CB

    style RF fill:#c9a84c,color:#000,stroke:#c9a84c
    style RAG fill:#1e3a5f,color:#fff
    style TF fill:#6b7685,color:#fff,stroke-dasharray:5
    style TD fill:#6b7685,color:#fff,stroke-dasharray:5
    style CB fill:#6b7685,color:#fff,stroke-dasharray:5
    style CCO fill:#2d5a2d,color:#fff

All current connections are plain JSON files on disk. RiskForge never calls external APIs.

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EU AI Act Article 9 Coverage

graph LR
    A9_1["Art. 9(1)<br/>Establish RMS"] --> REG["Register lifecycle<br/>Version history<br/>Audit log"]
    A9_2a["Art. 9(2)(a)<br/>Identify risks"] --> QB["Guided question bank<br/>8 dimensions · 37 questions"]
    A9_2b["Art. 9(2)(b)<br/>Estimate misuse risks"] --> PAT["Risk patterns<br/>6 Annex III scenarios"]
    A9_4["Art. 9(4)<br/>Risk measures"] --> MIT["Mitigation docs<br/>Vague-detection"]
    A9_6_8["Art. 9(6)–(8)<br/>Testing requirements"] --> TEST["riskforge tests generate<br/>Per-risk metric hints"]
    A9_9["Art. 9(9)<br/>Vulnerable groups"] --> VG["Dedicated questions<br/>Mandatory flag"]
    A9_10["Art. 9(10)<br/>Documentation"] --> AUD["Append-only JSONL<br/>SHA-256 hash chain"]

    style A9_1 fill:#1e3a5f,color:#fff
    style A9_2a fill:#1e3a5f,color:#fff
    style A9_2b fill:#1e3a5f,color:#fff
    style A9_4 fill:#1e3a5f,color:#fff
    style A9_6_8 fill:#1e3a5f,color:#fff
    style A9_9 fill:#1e3a5f,color:#fff
    style A9_10 fill:#1e3a5f,color:#fff

Cross-maps to: NIST AI RMF (GOVERN/MAP/MEASURE/MANAGE) · ISO/IEC 42001 (Clauses 6.1, 8.4, A.6–A.9) · Colorado AI Act SB 24-205 · Texas HB 1709

Disclaimer: RiskForge produces documented evidence for Article 9 compliance. It does not substitute for qualified legal counsel or notified body conformity assessment.

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Validation Gates

Before every export, riskforge validate runs 8 gates:

Gate	Check
G1	All 8 risk dimensions have at least one entry
G2	Article 6(2) Annex III self-classification documented
G3	All high-scoring risks mitigated or accepted with rationale
G4	Knowledge gaps have test requirements
G5	System metadata complete
G6	Assessor identity recorded
G7	Risk score distribution plausible (warns if all scores are low)
G8	No vague mitigation language detected

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Features

Feature	Detail
Offline-first	Zero outbound calls after pip install — enforced by pytest-socket CI gate
Hash-chained audit	Every mutation appended to audit.jsonl; riskforge verify exits code 2 on tampering
Schema-validated exports	Every JSON export validated against rmf.schema.json before writing
PDF export	WeasyPrint + Jinja2 — no LibreOffice or wkhtmltopdf required
Pattern matching	6 pre-built risk patterns for common Annex III use cases (credit scoring, hiring, facial recognition, medical imaging, content moderation, criminal risk assessment) — community contributions extend the library
Plugin extensible	Add question banks, exporters, adapters via pip install — no config edit required
Git-friendly state	YAML + JSONL files — human-readable, diff-able, merge-conflict-resolvable

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Contributing

The easiest contribution requires zero Python — edit a YAML file and open a PR.

Add a question to an existing dimension:

# src/riskforge/_data/question_bank/privacy.yaml
- id: PRIV-007
  text: "Does the system process special category data under GDPR Article 9?"
  guidance: "Special category data includes health, biometric, racial, or political data."
  annex_iii_categories: [essential_services, employment]
  default_likelihood_hint: 3
  default_severity_hint: 5
  article_refs: ["Art.9(2)(a)", "Art.10(3)"]
  nist_rmf_ref: "MAP 1.5"
  iso42001_ref: "Clause A.7"
  regulatory_status: settled

Add a risk pattern — edit src/riskforge/_data/patterns/patterns.yaml.

Fix a bug or add a feature — see CONTRIBUTING.md.

git clone https://github.com/aiexponenthq/riskforge
cd riskforge
make dev-setup   # pip install -e ".[dev]" + pre-commit install
make test        # 57 tests, all must pass
make lint        # ruff check + format

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Privacy

RiskForge makes zero outbound network connections in CLI mode, enforced in CI with pytest-socket --disable-socket.

RiskForge v1.0.0 | Apache 2.0 | Zero telemetry | aiexponent.com

Your AI system's risk data never leaves your machine unless you explicitly deploy the optional API server (pip install riskforge[server]).

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Releases

Version	Highlights
v1.0.0	First Production/Stable release. Click 8.3 regression fixed; LICENSE realigned to canonical SPDX; PRD amended to ship reality (37 questions, 6 patterns); coverage floor 24→55.
v0.1.4	CI fixes: lint version compat, format alignment, --sys-version rename
v0.1.3	Superseded by v0.1.4 (ruff format alignment)
v0.1.2	OSS hardening: LICENSE, CONTRIBUTING, SECURITY, issue templates, full integration tests
v0.1.1	riskforge assess fully implemented; PDF exporter fix; audit chain integrity fixes
v0.1.0	Initial release

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License

Apache 2.0 — free to use, modify, and distribute.

Built by AI Exponent LLC — [e-mail]

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Part of the AiExponent open-source AI governance toolchain: license-compliance-checker · rag-benchmarking · RiskForge