AI Governance Framework Crosswalk

Note (dated 2026-08-04): a chain composes independent computations performed in an analytical order. No step's output is carried into the next step by the execution model; each step computes from its own declared inputs. This is true of every chain on this site, including chains whose per-step copy below reads as a data handoff.

Map AI controls and evidence to NIST AI RMF Govern/Map/Measure/Manage functions with per-function coverage (art-174) → check GPAI provider obligations under EU AI Act Art. 53/55 and voluntary Code of Practice (art-175) → A-F AI governance readiness diagnostic across ISO 42001, NIST RMF, and EU AI Act convergence (art-176). Full multi-framework governance crosswalk. GPAI enforcement Aug 2026. Zero network, zero PII.

OpenChainGraph · 3 Steps compliance mandate NIST AI RMF EU AI Act Art.53/55 ISO 42001 GPAI W3C VC §13.11 Hash-Anchored §4 chain_depth:3 · Zero PII · v0.5.0

Chain Stages · 3 Steps

1ROOT · D0node

NIST AI RMF Function Mapper art-174

RMF function coverage feeds GPAI conformance checker

MCP Call · map_nist_ai_rmf_functions

{
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "name": "map_nist_ai_rmf_functions",
    "arguments": {
      "system_name": "Loan Underwriting AI",
      "controls": [
        {"id": "GOV-1.1", "description": "AI risk policy documented and approved"},
        {"id": "MAP-2.2", "description": "Impact assessment completed for affected stakeholders"},
        {"id": "MEA-4.1", "description": "Bias metrics monitored quarterly"},
        {"id": "MNG-3.2", "description": "Incident response plan tested annually"}
      ]
    }
  },
  "id": 1
}

↓

2MIDDLE · D1node

GPAI Code of Practice Conformance art-175

GPAI obligation status feeds AI governance readiness diagnostic

MCP Call · check_gpai_code_conformance

{
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "name": "check_gpai_code_conformance",
    "arguments": {
      "provider": {
        "name": "Acme AI Ltd",
        "model_type": "general_purpose",
        "flops_training": 1e23,
        "systemic_risk": false
      },
      "obligations": {
        "technical_documentation": true,
        "copyright_policy": true,
        "training_data_summary": false,
        "incident_reporting": true,
        "code_of_practice_signed": false
      }
    }
  },
  "id": 2
}

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3TERMINAL · D2node

AI Governance Readiness Diagnostic art-176

Exports A-F governance readiness grade with execution_hash — final stage

MCP Call · run_ai_governance_fit

{
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "name": "run_ai_governance_fit",
    "arguments": {
      "organization": "Acme AI Ltd",
      "frameworks": ["nist_ai_rmf", "iso_42001", "eu_ai_act"],
      "evidence": {
        "risk_policy": true,
        "impact_assessment": true,
        "bias_monitoring": true,
        "incident_response": true,
        "training_data_docs": false,
        "third_party_audit": false,
        "gpai_code_signed": false
      }
    }
  },
  "id": 3
}

Question: An internal audit found four NIST RMF measure controls unsupported. How far does our function coverage fall once they are withdrawn from the evidence? Run the AINumbers MCP tool `run_chain` with {"chain":"ai-governance-framework-crosswalk"} and read `art-174-nist-ai-rmf-function-mapper.overall_coverage`. Re-run with `inputs` for `art-174-nist-ai-rmf-function-mapper`, reusing the values that result echoed, with `evidence` changed so measure_analysis, measure_monitoring, measure_testing and measure_benchmarking are false. Compare the same field. Verify: call `verify_execution_hash` (https://mcp.ainumbers.co/mcp) with `claimed_hash` set to `composite_execution_hash` and the full `composite_artifact`. Ledger, for a human re-check: https://ledger.ainumbers.co/ PII rule: send synthetic or anonymised inputs only. The MCP server runs these kernels and logs no payloads. Chain page: https://ainumbers.co/chaingraph/chains/ai-governance-framework-crosswalk.html

AI Governance Framework Crosswalk

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