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EU AI Act compliance schema (schemas/eu-ai-act.yaml) — high-risk AI system documentation #99

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EU AI Act compliance schema (schemas/eu-ai-act.yaml) — high-risk AI system documentation#99
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Description

@avrabe
avrabe
opened on Apr 1, 2026

Context

The EU AI Act's provisions for high-risk AI systems become applicable on August 2, 2026 — 4 months away. Articles 9-15 and Annex IV mandate extensive documentation, risk management, and traceability for high-risk AI systems. Fines reach up to €35 million or 7% of global annual turnover for non-compliance.

Rivet already has the infrastructure for this:

  • STPA schema covers safety analysis (losses, hazards, UCAs, loss scenarios, control structure)
  • ASPICE schema covers the V-model traceability chain (stakeholder → system → software → verification)
  • Cybersecurity schema covers ISO 21434 / ASPICE SEC.1-4
  • Schema composition via extends and bridge schemas (Spec-driven development: schema packages, bridges, guide API, CRUD CLI #93) enables layering

What's missing: artifact types that directly map to the EU AI Act's Annex IV documentation requirements, plus traceability rules that enforce the Act's specific obligations.

Strategic value: No open-source traceability tool targets EU AI Act compliance for AI-enabled safety-critical systems. DOORS, Polarion, and codebeamer require expensive licenses and don't natively understand the Act's structure. Rivet can be the git-native, schema-validated compliance backbone — especially for teams already using STPA/ASPICE schemas.

EU AI Act Requirements Mapping

Annex IV: Technical Documentation (9 mandatory sections)

The schema maps each Annex IV section to one or more artifact types:

Annex IV Section Artifact Type(s) Key Fields Key Links
§1: General description ai-system-description intended-purpose, provider, version, hardware-deps, software-deps, deployment-forms —
§2: Development & design design-specification algorithms, design-choices, rationale, optimization-objectives, training-data-provenance satisfies → ai-system-description
§2 (data) data-governance-record data-sources, collection-method, labeling-method, preparation-steps, bias-assessment governs → design-specification
§2 (pre-trained) third-party-component provider, version, license, intended-use, known-limitations used-by → design-specification
§3: Monitoring & control monitoring-measure mechanism-type, logging-scope, alert-conditions, human-intervention-capability monitors → ai-system-description
§4: Performance metrics performance-evaluation metric-name, value, methodology, population-subgroups, bias-results evaluates → design-specification
§5: Risk management risk-assessment risk-description, likelihood, severity, affected-rights, risk-level leads-to → ai-system-description
§5 (mitigation) risk-mitigation measure-description, residual-risk, effectiveness-evidence mitigates → risk-assessment
§6: Lifecycle changes lifecycle-change change-type, version-from, version-to, rationale, compliance-impact modifies → design-specification
§7: Standards standards-reference standard-id, title, coverage-scope, partial-application-rationale applied-to → ai-system-description
§8: Declaration conformity-declaration declaration-date, notified-body, conformity-scope declares → ai-system-description
§9: Post-market monitoring post-market-plan monitoring-scope, drift-detection, incident-reporting, review-frequency monitors-post-market → ai-system-description

Articles 9-15: Obligation-specific artifact types

Article Requirement Artifact Type Purpose
Art. 9 Risk management system risk-management-process Documents the continuous, iterative risk management process
Art. 9(2a) Known risk identification risk-assessment Individual risk entries with likelihood/severity
Art. 9(2b) Foreseeable misuse risks misuse-risk Risks from reasonably foreseeable misuse
Art. 9(2c) Post-market emerging risks emerging-risk (links to post-market-plan) Risks discovered after deployment
Art. 9(4) Risk mitigation measures risk-mitigation Targeted measures for identified risks
Art. 10 Data governance data-governance-record Training/validation/test data provenance and quality
Art. 11 Technical documentation Schema itself (Annex IV coverage) The schema enforces Annex IV completeness
Art. 12 Record-keeping / logging logging-specification What events are logged, retention, access
Art. 13 Transparency transparency-record Information provided to deployers, user-facing docs
Art. 14 Human oversight human-oversight-measure Specific human intervention capabilities
Art. 15(1) Accuracy accuracy-evaluation Accuracy metrics and methodology
Art. 15(2) Robustness robustness-evaluation Resilience testing, adversarial evaluation
Art. 15(3) Cybersecurity cybersecurity-evaluation Security testing, vulnerability assessment

Traceability rules

traceability-rules:
  # Every AI system must have a complete risk management process
  - name: system-has-risk-management
    description: Every AI system description must be covered by a risk management process (Art. 9)
    source-type: ai-system-description
    required-backlink: manages-risk-for
    from-types: [risk-management-process]
    severity: error

  # Every identified risk must have a mitigation measure
  - name: risk-has-mitigation
    description: Every risk assessment must have at least one mitigation measure (Art. 9(4))
    source-type: risk-assessment
    required-backlink: mitigates
    from-types: [risk-mitigation]
    severity: error

  # Every AI system must have data governance documentation
  - name: system-has-data-governance
    description: Training/validation data must be governed (Art. 10)
    source-type: design-specification
    required-backlink: governs
    from-types: [data-governance-record]
    severity: error

  # Every AI system must have monitoring measures
  - name: system-has-monitoring
    description: AI system must have monitoring and control documentation (Art. 12)
    source-type: ai-system-description
    required-backlink: monitors
    from-types: [monitoring-measure]
    severity: error

  # Every AI system must have human oversight documentation
  - name: system-has-human-oversight
    description: Human oversight measures must be documented (Art. 14)
    source-type: ai-system-description
    required-backlink: overseen-by
    from-types: [human-oversight-measure]
    severity: error

  # Every AI system must have accuracy, robustness, cybersecurity evaluations
  - name: system-has-accuracy
    description: Accuracy metrics must be documented (Art. 15(1))
    source-type: design-specification
    required-backlink: evaluates
    from-types: [accuracy-evaluation, performance-evaluation]
    severity: error

  - name: system-has-robustness
    description: Robustness evaluation must be documented (Art. 15(2))
    source-type: design-specification
    required-backlink: evaluates
    from-types: [robustness-evaluation, performance-evaluation]
    severity: warning

  # Every system must reference applicable standards
  - name: system-has-standards
    description: Applicable harmonised standards must be listed (Annex IV §7)
    source-type: ai-system-description
    required-backlink: applied-to
    from-types: [standards-reference]
    severity: warning

  # Every system must have a conformity declaration
  - name: system-has-declaration
    description: EU declaration of conformity required (Annex IV §8)
    source-type: ai-system-description
    required-backlink: declares
    from-types: [conformity-declaration]
    severity: error

  # Every system must have post-market monitoring
  - name: system-has-post-market
    description: Post-market monitoring plan required (Art. 72, Annex IV §9)
    source-type: ai-system-description
    required-backlink: monitors-post-market
    from-types: [post-market-plan]
    severity: error

  # Risk management must cover foreseeable misuse
  - name: risk-covers-misuse
    description: Risk management should identify foreseeable misuse risks (Art. 9(2b))
    source-type: risk-management-process
    required-backlink: identified-by
    from-types: [misuse-risk]
    severity: warning

  # Transparency records must exist
  - name: system-has-transparency
    description: Transparency information for deployers must be documented (Art. 13)
    source-type: ai-system-description
    required-backlink: transparency-for
    from-types: [transparency-record]
    severity: error

Bridge schemas for STPA/ASPICE composition

The EU AI Act schema should compose with existing safety schemas:

# Bridge: eu-ai-act ↔ stpa
# Maps STPA safety analysis to EU AI Act risk management
schema:
  name: eu-ai-act-stpa-bridge
  version: "0.1.0"
  extends: [eu-ai-act, stpa]

link-types:
  - name: risk-identified-by-stpa
    inverse: stpa-identifies-risk
    description: Risk assessment derived from STPA hazard/loss analysis
    source-types: [risk-assessment]
    target-types: [hazard, sub-hazard, loss]

  - name: mitigation-from-constraint
    inverse: constraint-provides-mitigation
    description: Risk mitigation derived from STPA system constraint
    source-types: [risk-mitigation]
    target-types: [system-constraint, controller-constraint]

traceability-rules:
  - name: stpa-hazards-map-to-risks
    description: STPA hazards should be linked to EU AI Act risk assessments
    source-type: hazard
    required-backlink: risk-identified-by-stpa
    from-types: [risk-assessment]
    severity: warning
# Bridge: eu-ai-act ↔ aspice
# Maps ASPICE verification evidence to EU AI Act performance evaluation
schema:
  name: eu-ai-act-aspice-bridge
  version: "0.1.0"
  extends: [eu-ai-act, aspice]

link-types:
  - name: evaluation-from-verification
    inverse: verification-supports-evaluation
    description: Performance evaluation uses ASPICE verification evidence
    source-types: [performance-evaluation, accuracy-evaluation, robustness-evaluation]
    target-types: [sw-verification, sys-verification, verification-verdict]

traceability-rules:
  - name: evaluations-backed-by-verification
    description: Performance evaluations should reference ASPICE verification evidence
    source-type: performance-evaluation
    required-link: evaluation-from-verification
    target-types: [sw-verification, sys-verification, verification-verdict]
    severity: warning

Phases

Phase 1: Core schema + Annex IV artifact types

  • schemas/eu-ai-act.yaml with all artifact types from the mapping above
  • Link types specific to EU AI Act (manages-risk-for, governs, overseen-by, etc.)
  • Traceability rules for Articles 9-15 obligations
  • rivet init --schema eu-ai-act with starter artifacts
  • Example project in examples/eu-ai-act/

Phase 2: Bridge schemas

Phase 3: Compliance dashboard views

  • Annex IV completeness checklist in dashboard (which sections have coverage, which don't)
  • Risk register view (all risk-assessments with mitigation status)
  • Compliance matrix: Article × artifact type coverage

Phase 4: Export for notified bodies

  • rivet export --format eu-ai-act-report — structured report following Annex IV sections
  • rivet export --format eu-ai-act-dossier — full compliance dossier with all linked artifacts
  • PDF export option for submission to notified bodies

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