# RiskForge > EU AI Act Article 9 Risk Management System builder: 30 minutes to a signed, audit-trailed Risk Management File. Source: https://aiexponent.com/docs/riskforge · Content verified 2026-10-04 Guided 8-dimension risk assessment CLI with 37 questions drawn from EU AI Act Article 9 requirements, Annex III pattern matching, and SHA-256 hash-chained audit trail. Produces a Risk Management File (JSON + PDF) suitable for inclusion in your Annex IV technical documentation pack; not a substitute for notified-body conformity assessment. Approximately 30 minutes per assessment instead of weeks of consulting work. - EU AI Act: Article 9 (Risk Management) - PyPI package: `riskforge` v1.1.3 - Source: https://github.com/aiexponent/riskforge - Licence: Apache License 2.0 ## Quick start ```bash pip install riskforge ``` ```bash # 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. Record the Article 6(2) self-classification (required before export) riskforge system classify --confirm # 3. Run the guided 8-dimension risk assessment riskforge assess \ --assessor-name "Alice Chen" \ --assessor-role "AI Governance Lead" # 4. Mitigate or accept each high or critical risk (list them with: riskforge risk list ) riskforge risk accept --rationale "Residual within appetite after controls." # 5. Check completeness before export (8 validation gates) riskforge validate # 6. Export your Article 9 Risk Management File riskforge export --format pdf --output rmf.pdf riskforge export --format json --output rmf.json # 7. Verify the file has not been changed riskforge verify --file rmf.json ``` Full command reference: https://github.com/aiexponent/riskforge/tree/main/docs ## For coding agents Install riskforge from PyPI into a virtual environment (Python 3.11 or newer). Run `riskforge init --name --sys-version --purpose "" --provider --category ` and note the printed System ID. Then run `riskforge system classify --confirm` and `riskforge assess --assessor-name --assessor-role --answers answers.yaml` (the answers format is in the docs; without a terminal, assess needs the file). Mitigate or accept each high or critical risk shown by `riskforge risk list `, run `riskforge validate `, export with `riskforge export --format json --output rmf.json`, and check it with `riskforge verify --file rmf.json`. Docs: https://aiexponent.com/docs/riskforge.md ### Running without a terminal `riskforge assess` asks its questions interactively and needs a terminal; in a script or agent session pass `--answers answers.yaml`. Each answer is keyed by question ID; one answer per risk dimension lets gate G1 (all 8 dimensions covered) pass. This file is the credit-scoring example shipped with riskforge 1.1.3: ```yaml add_patterns: true answers: HS-001: { applies: yes, likelihood: 1, severity: 3 } FR-001: { applies: yes, likelihood: 3, severity: 4 } DI-001: { applies: yes, likelihood: 4, severity: 5 } PR-001: { applies: yes, likelihood: 3, severity: 4 } TR-001: { applies: yes, likelihood: 3, severity: 3 } HO-001: { applies: yes, likelihood: 2, severity: 4 } RO-001: { applies: yes, likelihood: 3, severity: 4 } DG-001: { applies: unknown } ``` Replace the likelihood and severity scores (1 to 5) with the user's own judgement; the values above are an example, not an assessment. Gate G3 needs every high or critical risk mitigated or accepted. Risk IDs are the 8-character IDs shown by `riskforge risk list `; `` is the full ID printed by `init`. To record a control instead of accepting the risk (`-c` takes the control type, for example preventive or detective): ```bash riskforge risk mitigate -m "" -c preventive \ --owner "" --residual-likelihood 2 --residual-severity 3 ``` Gate G8 flags vague mitigations (words such as "monitor" or "review"), so name the control and its threshold. Do not use `riskforge export --force`: it skips validation, and `riskforge verify` checks only that the file is unchanged, not that it is complete. Run every command from the folder where you ran `riskforge init`; the project state lives in `riskforge.yaml` and `.riskforge/` there. ## Features - 8 risk dimensions derived across Articles 9, 10, 13, 14 and 15, mapped to Article 9 obligations with 37 guided questions - Annex III pattern matching pre-populates risk items for known high-risk scenarios (credit scoring, hiring, facial recognition, medical diagnosis) - 5×5 likelihood × severity scoring matrix with automatic risk band classification - 8 pre-export validation gates (dimension coverage, vulnerable groups, vague mitigation detection) - SHA-256 hash-chained audit trail: tamper-evident, verifiable with `riskforge verify` (exits code 2 on corruption) - JSON, PDF (WeasyPrint), and Markdown export formats - Integration adapters for rag-benchmarking and TraceForge: import evidence directly - Cross-framework mapping: NIST AI RMF and ISO/IEC 42001 (per-question references in every export) - Zero outbound network calls in CLI mode, enforced by pytest-socket CI gate ## Regulatory foundation ### Article 9: Risk management system Status: UPCOMING. Applies from 2 Dec 2027 · deferred (statutory date before the Digital Omnibus: 2026-08-02). > 1. A risk management system shall be established, implemented, documented and maintained in relation to high-risk AI systems. > > 2. The risk management system shall be understood as a continuous iterative process planned and run throughout the entire lifecycle of a high-risk AI system, requiring regular systematic review and updating. It shall comprise the following steps: > > (a) the identification and analysis of the known and the reasonably foreseeable risks that the high-risk AI system can pose to health, safety or fundamental rights when the high-risk AI system is used in accordance with its intended purpose; > > (b) the estimation and evaluation of the risks that may emerge when the high-risk AI system is used in accordance with its intended purpose, and under conditions of reasonably foreseeable misuse; > > […] > > (d) the adoption of appropriate and targeted risk management measures designed to address the risks identified pursuant to point (a). > > […] > > 6. High-risk AI systems shall be tested for the purpose of identifying the most appropriate and targeted risk management measures. Testing shall ensure that high-risk AI systems perform consistently for their intended purpose and that they are in compliance with the requirements set out in this Section. Paragraphs quoted: 9(1), 9(2)(a), 9(2)(b), 9(2)(d), 9(6). Source: https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32024R1689 (retrieved 2026-10-04). - Omitted 9(2)(c): Post-market monitoring evaluation per Article 72 is out of scope for RiskForge; addressed by TraceForge (in development). - Omitted 9(3)–(8): Test-data, documentation, and testing provisions live in Art. 9(3) to 9(8). They are covered structurally by the Risk Management File output rather than quoted here. Penalty: Up to €15M or 3% of global annual turnover, whichever is higher (Article 99(4), https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32024R1689). Article 9 applies from 2 December 2027 for high-risk AI systems. Regulation (EU) 2026/1744, the Digital Omnibus on AI (in force since 27 July 2026), deferred that date from 2 August 2026. What the article requires is a documented, lifecycle-long risk management system, not a one-time assessment. Failure to maintain it routes through the Article 16 provider obligations and is sanctionable up to €15M or 3% of global annual turnover under Article 99(4). The operational consequence: risk management must produce versioned, reviewable artefacts mapped to identified hazards, with testing evidence sufficient to defend the residual-risk judgement. How RiskForge addresses this: - 9(1): Generates a versioned risk-management-system file: hazard register, risk owners, review cadence, change history - 9(2)(a): Structured hazard identification across health, safety and fundamental-rights dimensions with intended-purpose framing - 9(2)(b): Reasonably-foreseeable-misuse scenario library with likelihood × severity scoring and mitigation linkage - 9(2)(d): Maps each identified risk to a targeted mitigation control and tracks residual-risk acceptance with sign-off trail - 9(6): Test-plan generator tying each hazard to a measurable test, with prior-defined metrics and probabilistic thresholds (Art. 9(8)) ## FAQ ### What does EU AI Act Article 9 require? A documented, lifecycle-long risk management system for high-risk AI systems, not a one-time assessment. It must identify foreseeable risks to health, safety, and fundamental rights; estimate and evaluate them under intended use and reasonably foreseeable misuse; adopt targeted mitigation measures; and produce testing evidence sufficient to defend the residual-risk judgement. Source: Regulation (EU) 2024/1689 Article 9(1)–(2)(a)–(b)(d), 9(6). ### When does Article 9 become enforceable? Article 9 obligations for high-risk AI systems apply from 2 December 2027, deferred from the original 2 August 2026 statutory date by the Digital Omnibus on AI, Regulation (EU) 2026/1744, which was published in the Official Journal on 24 July 2026 and has been in force since 27 July 2026. Source: Regulation (EU) 2024/1689 Article 113, as amended by Regulation (EU) 2026/1744. ### How long does a complete Risk Management File take with RiskForge? Approximately 30 minutes for an interactive 8-dimension assessment with 37 guided questions, depending on the complexity of the system being assessed. The output is a JSON + PDF Risk Management File suitable for inclusion in your Annex IV technical documentation pack, not a substitute for notified-body conformity assessment. ### Is RiskForge a notified-body conformity assessment? No. RiskForge produces documented evidence supporting an Article 9 risk management system. Conformity assessment by a notified body, where required, is a separate process performed by accredited entities. RiskForge output is one input to that process, not a replacement for it. ### What scoring methodology does RiskForge use? A 5×5 likelihood × severity matrix with automatic risk-band classification, applied per identified risk. Annex III pattern matching pre-populates risk items for known high-risk scenarios (credit scoring, hiring, facial recognition, medical diagnosis). ### Does RiskForge cross-map to NIST AI RMF and ISO/IEC 42001? Yes. Each risk-management dimension carries per-question references to NIST AI RMF GOVERN/MAP/MEASURE/MANAGE categories and ISO/IEC 42001 controls, emitted in every export. A single assessment produces evidence reusable across both frameworks. ### What is the penalty for Article 9 non-compliance? Up to €15M or 3% of global annual turnover, whichever is higher, under Article 99(4). The Article 16 provider obligation chain routes Article 9 failures through this penalty band. ### Is RiskForge free? Yes. Apache 2.0 licensed, free for any use including commercial. No telemetry: outbound network calls are blocked at CI level via pytest-socket. ### Can I customize the question bank for sector-specific risks? Yes. The question bank is plug-in based via Python entry points. The core 8 dimensions cover the regulatory baseline; sector-specific additions (medical devices, financial services) are extensible through user-supplied bank YAML files. ### How is the audit trail tamper-evident? Every change is recorded with a SHA-256 hash chained to the previous entry. `riskforge verify` recomputes the chain and exits with code 2 if any link is broken, making tampering or partial deletion CI-detectable. ## Known limitations - Produces documented evidence for Article 9 compliance. It does not substitute for qualified legal counsel or notified body conformity assessment. - Question bank covers 37 questions across 8 risk dimensions; specialised sector questions (e.g. medical devices) may require custom additions. - Interactive assessment requires a terminal; CI/CD integration uses the engine layer directly. - PDF export via WeasyPrint: some complex layouts may require HTML/CSS customisation. - Apache 2.0 licensed; no warranty of legal compliance. ## Contributing Issues: https://github.com/aiexponent/riskforge/issues · Contributing guide: https://github.com/aiexponent/riskforge/blob/main/CONTRIBUTING.md --- Not legal advice. Not a notified body. The tools produce evidence, not conformity assessment. All docs as Markdown: https://aiexponent.com/llms.txt · Guide for coding agents: https://aiexponent.com/agents.md