CyberPulseコンプライアンス – v2 バッチパイプライン
上級
これは自動化ワークフローで、18個のノードを含みます。主にSet, Code, Merge, GoogleSheets, ManualTriggerなどのノードを使用。 CyberPulse、GPT-4o、Google Sheetsを使用した自動コンプライアンスコントロールスコアリング
前提条件
- •Google Sheets API認証情報
- •OpenAI API Key
カテゴリー
-
ワークフロープレビュー
ノード接続関係を可視化、ズームとパンをサポート
ワークフローをエクスポート
以下のJSON設定をn8nにインポートして、このワークフローを使用できます
{
"id": "6yJ0KXSSGqSb9FY6",
"meta": {
"instanceId": "6feff41aadeb8409737e26476f9d0a45f95eec6a9c16afff8ef87a662455b6df",
"templateCredsSetupCompleted": true
},
"name": "CyberPulse Compliance – v2 batch pipeline",
"tags": [],
"nodes": [
{
"id": "1ae2a107-6515-4dbe-a3b8-4c3fd5d64cce",
"name": "手動トリガー",
"type": "n8n-nodes-base.manualTrigger",
"position": [
-1008,
-608
],
"parameters": {},
"typeVersion": 1
},
{
"id": "2360ade2-1686-4554-beb3-76ba59e16408",
"name": "シートに行を追加",
"type": "n8n-nodes-base.googleSheets",
"position": [
2208,
-336
],
"parameters": {
"columns": {
"value": {
"score": "={{ $json.score }}",
"status": "={{ $json.status }}",
"rationale": "={{ $json.rationale }}",
"timestamp": "={{ $json.timestamp }}",
"ai_summary": "={{ $json.ai_summary }}",
"categories": "={{ $json.categories }}",
"confidence": "={{ $json.confidence }}",
"control_id": "={{ $json.control_id }}",
"evaluation": "={{ $json.evaluation }}",
"ai_findings": "={{ $json.ai_findings }}",
"mapped_count": "={{ $json.mapped_count }}",
"mapping_flat": "={{ $json.mapping_flat }}",
"response_text": "={{ $json.response_text }}",
"engine_version": "={{ $json.engine_version }}",
"evidence_count": "={{ $json.evidence_count }}",
"evidence_url_1": "={{ $json.evidence_url_1 }}",
"evidence_url_2": "={{ $json.evidence_url_2 }}",
"evidence_url_3": "={{ $json.evidence_url_3 }}",
"evidence_url_4": "={{ $json.evidence_url_4 }}",
"ai_recommendations": "={{ $json.ai_recommendations }}",
"control_description": "={{ $json.control_description }}",
"frameworks_selected": "={{ $json.frameworks_selected }}",
"implementation_notes": "={{ $json.implementation_notes }}"
},
"schema": [
{
"id": "timestamp",
"type": "string",
"display": true,
"required": false,
"displayName": "timestamp",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "control_id",
"type": "string",
"display": true,
"required": false,
"displayName": "control_id",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "control_description",
"type": "string",
"display": true,
"required": false,
"displayName": "control_description",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "response_text",
"type": "string",
"display": true,
"required": false,
"displayName": "response_text",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "implementation_notes",
"type": "string",
"display": true,
"required": false,
"displayName": "implementation_notes",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "evidence_url_1",
"type": "string",
"display": true,
"required": false,
"displayName": "evidence_url_1",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "evidence_url_2",
"type": "string",
"display": true,
"required": false,
"displayName": "evidence_url_2",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "evidence_url_3",
"type": "string",
"display": true,
"required": false,
"displayName": "evidence_url_3",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "evidence_url_4",
"type": "string",
"display": true,
"required": false,
"displayName": "evidence_url_4",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "status",
"type": "string",
"display": true,
"required": false,
"displayName": "status",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "evaluation",
"type": "string",
"display": true,
"required": false,
"displayName": "evaluation",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "score",
"type": "string",
"display": true,
"required": false,
"displayName": "score",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "confidence",
"type": "string",
"display": true,
"required": false,
"displayName": "confidence",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "rationale",
"type": "string",
"display": true,
"required": false,
"displayName": "rationale",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "categories",
"type": "string",
"display": true,
"required": false,
"displayName": "categories",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "evidence_count",
"type": "string",
"display": true,
"required": false,
"displayName": "evidence_count",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "mapped_count",
"type": "string",
"display": true,
"required": false,
"displayName": "mapped_count",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "mapping_flat",
"type": "string",
"display": true,
"required": false,
"displayName": "mapping_flat",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "frameworks_selected",
"type": "string",
"display": true,
"required": false,
"displayName": "frameworks_selected",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "engine_version",
"type": "string",
"display": true,
"required": false,
"displayName": "engine_version",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "ai_summary",
"type": "string",
"display": true,
"required": false,
"displayName": "ai_summary",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "ai_findings",
"type": "string",
"display": true,
"required": false,
"displayName": "ai_findings",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "ai_recommendations",
"type": "string",
"display": true,
"required": false,
"displayName": "ai_recommendations",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "priority",
"type": "string",
"display": true,
"required": false,
"displayName": "priority",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "owner",
"type": "string",
"display": true,
"required": false,
"displayName": "owner",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "due_date",
"type": "string",
"display": true,
"required": false,
"displayName": "due_date",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "ticket_id",
"type": "string",
"display": true,
"required": false,
"displayName": "ticket_id",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "next_action",
"type": "string",
"display": true,
"required": false,
"displayName": "next_action",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "evidence_gap_flag",
"type": "string",
"display": true,
"required": false,
"displayName": "evidence_gap_flag",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "policy_gap_flag",
"type": "string",
"display": true,
"required": false,
"displayName": "policy_gap_flag",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "last_run_id",
"type": "string",
"display": true,
"required": false,
"displayName": "last_run_id",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "source_sheet_row",
"type": "string",
"display": true,
"required": false,
"displayName": "source_sheet_row",
"defaultMatch": false,
"canBeUsedToMatch": true
}
],
"mappingMode": "defineBelow",
"matchingColumns": [],
"attemptToConvertTypes": false,
"convertFieldsToString": false
},
"options": {},
"operation": "append",
"sheetName": {
"__rl": true,
"mode": "list",
"value": 1117838353,
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/1owd3qVXCO34EhvBO5Vr3d2Pn2_QQXt0rNl_kroSmf0I/edit#gid=1117838353",
"cachedResultName": "controls_results_template.csv"
},
"documentId": {
"__rl": true,
"mode": "list",
"value": "1owd3qVXCO34EhvBO5Vr3d2Pn2_QQXt0rNl_kroSmf0I",
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/1owd3qVXCO34EhvBO5Vr3d2Pn2_QQXt0rNl_kroSmf0I/edit?usp=drivesdk",
"cachedResultName": "controls_results_template"
}
},
"credentials": {
"googleSheetsOAuth2Api": {
"id": "SHFVDGHEaA6jzLc4",
"name": "Google Sheets account"
}
},
"typeVersion": 4.7
},
{
"id": "46fe1437-f8b0-4ede-9cc6-23958351755a",
"name": "シートの行を取得",
"type": "n8n-nodes-base.googleSheets",
"position": [
-992,
-368
],
"parameters": {
"options": {},
"sheetName": {
"__rl": true,
"mode": "list",
"value": 1182991363,
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/1Gcak2OCpo2vnDkB2W49xz4TD762DGCY3ZA6Pkfv82nM/edit#gid=1182991363",
"cachedResultName": "Sheet1"
},
"documentId": {
"__rl": true,
"mode": "list",
"value": "1Gcak2OCpo2vnDkB2W49xz4TD762DGCY3ZA6Pkfv82nM",
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/1Gcak2OCpo2vnDkB2W49xz4TD762DGCY3ZA6Pkfv82nM/edit?usp=drivesdk",
"cachedResultName": "controls_input_mock_100_rows"
}
},
"credentials": {
"googleSheetsOAuth2Api": {
"id": "SHFVDGHEaA6jzLc4",
"name": "Google Sheets account"
}
},
"typeVersion": 4.7
},
{
"id": "2f4910f6-3fdf-4133-b714-b6752d5bdb94",
"name": "説明と推奨",
"type": "@n8n/n8n-nodes-langchain.openAi",
"position": [
640,
-288
],
"parameters": {
"modelId": {
"__rl": true,
"mode": "list",
"value": "gpt-4o-mini",
"cachedResultName": "GPT-4O-MINI"
},
"options": {},
"messages": {
"values": [
{
"role": "system",
"content": "=Use only the provided fields. Do not invent evidence, numbers, or frameworks.\nReturn a JSON object with keys exactly:\n- ai_summary (string)\n- ai_findings (array of 3 short strings), no bullets, no dashes, no numbering, no checkboxes.\n- ai_recommendations (array of 3 short, actionable strings), no bullets, no dashes, no numbering, no checkboxes.\nNo other keys.\n"
},
{
"content": "={\n \"timestamp\": \"{{ $('Edit Fields').item.json.timestamp }}\",\n \"control_id\": \"{{ $('Edit Fields').item.json.control_id }}\",\n \"control_description\": \"{{ $('Edit Fields').item.json.control_description }}\",\n \"response_text\": \"{{ $('Edit Fields').item.json.response_text }}\",\n \"implementation_notes\": \"{{$json.implementation_notes || ''}}\",\n\n \"status\": \"{{$json.status}}\",\n \"evaluation\": \"{{$json.evaluation}}\",\n \"score\": {{ +$json.score }},\n \"confidence\": {{ +$json.confidence }},\n \"rationale\": \"{{$json.rationale}}\",\n\n \"evidence_count\": {{\n Array.isArray($json.evidence)\n ? $json.evidence.filter(u => u && String(u).trim()).length\n : ([\n $json.evidence_url_1,\n $json.evidence_url_2,\n $json.evidence_url_3,\n $json.evidence_url_4\n ].filter(u => u && String(u).trim()).length || ($json.evidence_count ?? 0))\n}},\n\n \"mapped_count\": {{ Array.isArray($json.mapped_requirements) ? $json.mapped_requirements.length : ($json.mapped_count ?? 0) }},\n \"mapping_flat\": \"{{ Array.isArray($json.mapped_requirements) ? $json.mapped_requirements.map(m => [m.framework, m.clause, m.title].filter(Boolean).join(': ')).join(' | ') : ($json.mapping_flat || '') }}\",\n \"categories\": \"{{ Array.isArray($json.categories) ? $json.categories.join(', ') : ($json.categories || '') }}\",\n \"frameworks_selected\": \"{{ Array.isArray($json.mapped_requirements) ? [...new Set($json.mapped_requirements.map(m => m.framework).filter(Boolean))].join(', ') : ($json.frameworks_selected || '') }}\",\n \"engine_version\": \"{{$json.engine_version || ''}}\",\n\n \"format_instructions\": \"Return ai_summary exactly as: 'Status: {status}. Evaluation: {evaluation}. Score: {score}. Confidence: {confidence}. Evidence items: {evidence_count}. Categories: {categories}. Mappings: {mapping_flat}'. Then return ai_findings as a single string with 3 short bullets (prefix each with '• ') grounded in rationale/categories. Then return ai_recommendations as a single string with 3 short, actionable bullets (prefix each with '• ') tied to mapping_flat and evidence_count. Also compute priority, next_action, evidence_gap_flag, policy_gap_flag using the rules below.\\n\\nRules:\\n- priority:\\n - P1 if (status == 'Non-Compliant' && mapped_count >= 3) OR (score < 50 && evidence_count == 0)\\n - P2 if (status in ['Non-Compliant','Partial'] && (score >= 50 && score < 75)) OR (evidence_count <= 1)\\n - P3 if (status == 'Compliant' && (confidence < 60 || evidence_count < 2))\\n - else P4\\n- next_action:\\n - 'implement' if status == 'Non-Compliant'\\n - 'remediate' if status == 'Partial'\\n - 'monitor' if status == 'Compliant' and (confidence < 60 || evidence_count < 2)\\n - else 'review'\\n- evidence_gap_flag: 'yes' if evidence_count == 0 OR evidence_count == 1, else 'no'\\n- policy_gap_flag: 'yes' if ('policy' appears in (response_text or rationale) case-insensitive) OR categories contains 'policy', else 'no'\"\n}\n"
}
]
},
"jsonOutput": true
},
"credentials": {
"openAiApi": {
"id": "gcqJ07oLkr9oSi82",
"name": "OpenAi account"
}
},
"typeVersion": 1.8
},
{
"id": "1920b78c-97ac-4303-a055-e2541cf12f29",
"name": "解析 + 各項目に添付",
"type": "n8n-nodes-base.code",
"position": [
1696,
-336
],
"parameters": {
"mode": "runOnceForEachItem",
"jsCode": "// Helpers\nfunction isNonEmpty(x){ if(x===undefined||x===null) return false; if(typeof x==='number') return true; return String(x).trim()!==''; }\nfunction prefer(...vals){ for(const v of vals){ if(isNonEmpty(v)) return v; } return ''; }\nfunction safeNum(x, def=''){ const n = Number(x); return Number.isFinite(n) ? n : def; }\n\n// Strip any leading bullets / dashes / numbers / checkboxes before we add our own bullets\nfunction stripLeadingMarkers(s){\n return String(s).replace(/^\\s*(?:[-*•\\u2022]+|\\d+[.)]|[✓✔✗✘xX\\[\\]\\(\\)])\\s*/u, '');\n}\nfunction asArray(x){\n if(!x) return [];\n if(Array.isArray(x)) return x.filter(Boolean).map(v => stripLeadingMarkers(v));\n return String(x).split(/\\r?\\n+/).map(v => stripLeadingMarkers(v)).filter(Boolean);\n}\nfunction bullets(x){\n const a = asArray(x);\n return a.length ? a.map(s => `• ${s}`).join('\\n') : '';\n}\n\n// Current item from Merge\nconst i = $json;\n// Originals straight from \"Edit Fields\" (for fields the merge might not include)\nconst src = $(\"Edit Fields\").item?.json ?? {};\n\n// ---- Ingest (preserve originals)\nconst timestamp = prefer(i.timestamp, src.timestamp, new Date().toISOString());\nconst control_id = prefer(i.control_id, src.control_id);\nconst control_description = prefer(i.control_description, src.control_description);\nconst response_text = prefer(i.response_text, src.response_text);\nconst implementation_notes= prefer(i.implementation_notes, src.implementation_notes);\nconst evidence_url_1 = prefer(i.evidence_url_1, src.evidence_url_1);\nconst evidence_url_2 = prefer(i.evidence_url_2, src.evidence_url_2);\nconst evidence_url_3 = prefer(i.evidence_url_3, src.evidence_url_3);\nconst evidence_url_4 = prefer(i.evidence_url_4, src.evidence_url_4);\n\n// ---- Scoring & mapping\nconst status = prefer(i.status, 'Unknown');\nconst evaluation = prefer(i.evaluation, status);\nconst score = safeNum(i.score);\nconst confidence = safeNum(i.confidence);\nconst rationaleIn = prefer(i.rationale, i.reason);\n\n// categories may be array or string\nconst categoriesStr = Array.isArray(i.categories) ? i.categories.join(', ') : prefer(i.categories);\n\n// evidence: prefer i.evidence (array), else derive from URL fields, then filter empties\nlet evidenceArr = [];\nif (Array.isArray(i.evidence)) {\n evidenceArr = i.evidence.filter(u => u && String(u).trim());\n} else {\n evidenceArr = [evidence_url_1, evidence_url_2, evidence_url_3, evidence_url_4]\n .filter(u => u && String(u).trim());\n}\nconst evidence_count = isNonEmpty(i.evidence_count) ? safeNum(i.evidence_count, evidenceArr.length) : evidenceArr.length;\n\n// mapped requirements\nconst mappedReqs = Array.isArray(i.mapped_requirements) ? i.mapped_requirements : [];\nconst mapped_count = isNonEmpty(i.mapped_count) ? safeNum(i.mapped_count, mappedReqs.length) : mappedReqs.length;\nconst mapping_flat = isNonEmpty(i.mapping_flat)\n ? String(i.mapping_flat)\n : mappedReqs.map(m => [m.framework, m.clause, m.title].filter(Boolean).join(': ')).join(' | ');\n\n// frameworks selected (pretty commas)\nconst frameworks_selected = (isNonEmpty(i.frameworks_selected)\n ? String(i.frameworks_selected)\n : [...new Set(mappedReqs.map(m => m.framework).filter(Boolean))].join(', ')\n).replace(/,\\s*/g, ', ');\n\nconst engine_version = prefer(i.engine_version, i.version);\n\n// ---- AI outputs (handle message.content object OR JSON string; also accept top-level)\nlet ai_summary = i.ai_summary;\nlet ai_findings_any = i.ai_findings;\nlet ai_recommendations_any = i.ai_recommendations;\n\nif ((!ai_summary || (!ai_findings_any && !ai_recommendations_any)) && i.message?.content) {\n if (typeof i.message.content === 'object') {\n const c = i.message.content;\n ai_summary = ai_summary ?? c.ai_summary;\n ai_findings_any = ai_findings_any ?? c.ai_findings;\n ai_recommendations_any = ai_recommendations_any ?? c.ai_recommendations;\n } else {\n try {\n const parsed = JSON.parse(i.message.content);\n ai_summary = ai_summary ?? parsed.ai_summary;\n ai_findings_any = ai_findings_any ?? parsed.ai_findings;\n ai_recommendations_any = ai_recommendations_any ?? parsed.ai_recommendations;\n } catch {}\n }\n}\n\n// Normalize to single strings for the sheet (no double bullets)\nconst ai_findings = bullets(ai_findings_any);\nconst ai_recommendations = bullets(ai_recommendations_any);\n\n// Sync the item count inside rationale (e.g., replace \"(4 items)\" with \"(3 items)\")\nlet rationale = rationaleIn;\nif (isNonEmpty(rationale)) {\n rationale = rationale.replace(/\\(\\s*\\d+\\s*items?\\s*\\)/i, `(${evidence_count} items)`);\n}\n\n// Synthesize summary if missing (deterministic)\nif (!isNonEmpty(ai_summary)) {\n ai_summary = `Status: ${status}. Evaluation: ${evaluation}. Score: ${score}. `\n + `Confidence: ${confidence}. Evidence items: ${evidence_count}. `\n + `Categories: ${categoriesStr}. Mappings: ${mapping_flat}`;\n}\n\n// Return final row payload\nreturn {\n json: {\n // Ingest\n timestamp,\n control_id,\n control_description,\n response_text,\n implementation_notes,\n evidence_url_1,\n evidence_url_2,\n evidence_url_3,\n evidence_url_4,\n\n // Scoring & mapping\n status,\n evaluation,\n score,\n confidence,\n rationale,\n categories: categoriesStr,\n evidence_count,\n mapped_count,\n mapping_flat,\n frameworks_selected,\n engine_version,\n\n // AI (strings)\n ai_summary,\n ai_findings,\n ai_recommendations\n }\n};"
},
"typeVersion": 2
},
{
"id": "23954e15-29e1-49d3-9614-772e33562fc4",
"name": "項目をループ処理",
"type": "n8n-nodes-base.splitInBatches",
"position": [
160,
-368
],
"parameters": {
"options": {}
},
"typeVersion": 3
},
{
"id": "1f2dda49-fde7-44f8-8efd-e6ffd73e4e96",
"name": "フィールドを編集",
"type": "n8n-nodes-base.set",
"position": [
-672,
-368
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "87318d44-cd2c-47d8-850a-c0b152d6b455",
"name": "row_number",
"type": "string",
"value": "={{ $json.row_number }}"
},
{
"id": "9eab7a02-e58e-46a8-85cb-d2a41693759c",
"name": "timestamp",
"type": "string",
"value": "={{ $json.timestamp }}"
},
{
"id": "6d3fa2f3-5e8b-45c6-80f6-e9b713fd82e4",
"name": "control_description",
"type": "string",
"value": "={{ $json.control_description }}"
},
{
"id": "e0b7a487-bc52-40d9-80a6-5bc2a13c19a0",
"name": "response_text",
"type": "string",
"value": "={{ $json.response_text }}"
},
{
"id": "b6347812-76af-454b-8869-81c47f4c90a8",
"name": "evidence_url_1",
"type": "string",
"value": "={{ $json.evidence_url_1 }}"
},
{
"id": "f8784a98-ead8-4af2-a1ca-42aa2fcc6032",
"name": "evidence_url_2",
"type": "string",
"value": "={{ $json.evidence_url_2 }}"
},
{
"id": "d91dceb0-e1a7-4322-89a9-041a34e00f52",
"name": "evidence_url_3",
"type": "string",
"value": "={{ $json.evidence_url_3 }}"
},
{
"id": "14d6374b-5191-4f4f-85b0-d443a9e404ee",
"name": "evidence_url_4",
"type": "string",
"value": "={{ $json.evidence_url_4 }}"
},
{
"id": "b8bd82a7-c19c-458b-a0cb-5cf2941efc0c",
"name": "implementation_notes",
"type": "string",
"value": "={{ $json.implementation_notes }}"
}
]
},
"includeOtherFields": true
},
"typeVersion": 3.4
},
{
"id": "af2a5b70-b8e7-4ab4-b9cb-29da669d1474",
"name": "マージ1",
"type": "n8n-nodes-base.merge",
"position": [
1216,
-336
],
"parameters": {
"mode": "combine",
"options": {
"includeUnpaired": true
},
"combineBy": "combineByPosition"
},
"typeVersion": 3.2
},
{
"id": "d2232908-5186-4e0a-b771-7230028b8b43",
"name": "CyberPulse Compliance (Dev)",
"type": "n8n-nodes-cyberpulse-compliance-dev.cyberPulseCompliance",
"position": [
-288,
-368
],
"parameters": {
"controlText": "=={{ $json.response_text + ($json.implementation_notes ? (' ' + $json.implementation_notes) : '') }}",
"crosswalkUrl": "https://gist.githubusercontent.com/gitadta/c6b7b69ae2a00f2a67e3bbac4b6648d4/raw/238ce80b0252702d4e6e9c19015bf958d0a0bad6/crosswalk.json",
"evidenceUrls": [
"={{ $json.evidence_url_1 }}",
"={{ $json.evidence_url_2 }}",
"={{ $json.evidence_url_3 }}",
"={{ $json.evidence_url_4 }}"
]
},
"credentials": {
"cyberPulseHttpHeaderAuthApi": {
"id": "s8WtV23qQ1z0bVTT",
"name": "CyberPulse HTTP Header Auth account"
}
},
"typeVersion": 6
},
{
"id": "a7a4f9e1-df82-4776-827b-e89177d9c935",
"name": "付箋",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1328,
-704
],
"parameters": {
"color": 4,
"width": 528,
"height": 512,
"content": "\n\n🟢 Manual Trigger — Start/diagnostics\n\nReceives POST and starts the run; echoes runId for tracing\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n🟩 Get row(s) in sheet — Read inputs\n\nLoads model/routing settings from\n the config sheet."
},
"typeVersion": 1
},
{
"id": "95cc8cf7-0b2b-4e33-974c-bcb23d07c265",
"name": "付箋1",
"type": "n8n-nodes-base.stickyNote",
"position": [
-784,
-576
],
"parameters": {
"color": 5,
"width": 400,
"height": 384,
"content": "\n\n\n\n\n🟦 Edit Fields — Normalize columns\n\nMaps incoming keys, trims text, and sets safe defaults."
},
"typeVersion": 1
},
{
"id": "f98d67c1-31e3-4274-a0b0-41812290b18a",
"name": "付箋2",
"type": "n8n-nodes-base.stickyNote",
"position": [
-368,
-576
],
"parameters": {
"width": 432,
"height": 384,
"content": "\n\n🟨 CyberPulse Compliance (Dev) — Score + map\n\nScores and maps each control using control text + implementation notes + up to 4 evidence URLs and selected frameworks.\nOutputs score (0–100), status, confidence, binary evaluation, categories, crosswalk mappings, and adds gaps/actions when evidence is weak or missing."
},
"typeVersion": 1
},
{
"id": "0777506c-4b63-45ae-a27e-2c4fa519947c",
"name": "付箋3",
"type": "n8n-nodes-base.stickyNote",
"position": [
80,
-576
],
"parameters": {
"color": 6,
"width": 432,
"height": 384,
"content": "\n\n🟪 Loop Over Items — Iterate per control\n\nIterates each control independently to run LLM → parse → append, preserving per-row context and emitting one result per input."
},
"typeVersion": 1
},
{
"id": "f5161050-7010-43d6-aadc-eec7daae85d0",
"name": "付箋4",
"type": "n8n-nodes-base.stickyNote",
"position": [
528,
-576
],
"parameters": {
"color": 5,
"width": 512,
"height": 384,
"content": "\n\n🟦 Explain & Recommend (Message Model) — Exec summary\n\nGenerates a strict-JSON executive summary—ai_summary, three ai_findings, and three ai_recommendations—from the control’s status, score, confidence, categories, evidence, and framework mappings."
},
"typeVersion": 1
},
{
"id": "ed04125f-e8d8-4a05-afe0-3f8844f07f5e",
"name": "付箋5",
"type": "n8n-nodes-base.stickyNote",
"position": [
1056,
-576
],
"parameters": {
"color": 3,
"width": 464,
"height": 384,
"content": "\n\n🟧 Merge1 — Combine model + original\n\nCombines CyberPulse scoring/mapping with the LLM summary by position, producing one merged item per row."
},
"typeVersion": 1
},
{
"id": "16c54ee5-0dc1-4374-b2f9-ca831de1c1a4",
"name": "付箋6",
"type": "n8n-nodes-base.stickyNote",
"position": [
1536,
-576
],
"parameters": {
"width": 464,
"height": 384,
"content": "\n\n🟨 Parse + attach to each item — Final shaping\n\nMerges CyberPulse scores/mappings with the LLM output by index into a single unified row."
},
"typeVersion": 1
},
{
"id": "b4e8b1ee-95ad-4a8b-bd3b-2c07ecab42b3",
"name": "付箋7",
"type": "n8n-nodes-base.stickyNote",
"position": [
2016,
-576
],
"parameters": {
"color": 4,
"width": 496,
"height": 384,
"content": "\n\n🟩 Append row in sheet — Write results\n\nAppends one result row per item to the results sheet with core, scoring, mapping, and AI fields, leaving future columns blank."
},
"typeVersion": 1
},
{
"id": "297e3ea4-35bd-4bc5-a851-ecf0f8dda5bc",
"name": "付箋8",
"type": "n8n-nodes-base.stickyNote",
"position": [
192,
-912
],
"parameters": {
"color": 7,
"width": 784,
"height": 288,
"content": "\n\nWhat is CyberPulse Agent workflow ?\n\nAutomates evidence-aware control scoring (0–100) with deterministic gates and confidence from evidence/text quality.\nReads controls from Google Sheets (text, up to 4 evidence URLs, notes) and classifies, scores, and maps via the CyberPulse node.\nGenerates board-ready AI outputs per control: one-paragraph summary, 3 findings, and 3 actionable recommendations.\nWrites normalized, analytics-ready rows back to a results sheet with flattened framework mappings and detected categories.\nCovers ISO 27001, NIST CSF, SOC 2, PCI DSS, Essential Eight, GDPR; secure, scalable in n8n with tunable weights/thresholds."
},
"typeVersion": 1
}
],
"active": false,
"pinData": {},
"settings": {
"executionOrder": "v1"
},
"versionId": "8a012f28-964c-4087-9b74-7c24a4d8f76e",
"connections": {
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"1f2dda49-fde7-44f8-8efd-e6ffd73e4e96": {
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[],
[
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{
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"type": "main",
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]
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},
"2360ade2-1686-4554-beb3-76ba59e16408": {
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[
{
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"type": "main",
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}
]
]
},
"2f4910f6-3fdf-4133-b714-b6752d5bdb94": {
"main": [
[
{
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}
]
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},
"46fe1437-f8b0-4ede-9cc6-23958351755a": {
"main": [
[
{
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"type": "main",
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}
]
]
},
"d2232908-5186-4e0a-b771-7230028b8b43": {
"main": [
[
{
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"type": "main",
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}
]
]
},
"1920b78c-97ac-4303-a055-e2541cf12f29": {
"main": [
[
{
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"type": "main",
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}
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}
}
}よくある質問
このワークフローの使い方は？
上記のJSON設定コードをコピーし、n8nインスタンスで新しいワークフローを作成して「JSONからインポート」を選択、設定を貼り付けて認証情報を必要に応じて変更してください。
このワークフローはどんな場面に適していますか？
上級
有料ですか？
このワークフローは完全無料です。ただし、ワークフローで使用するサードパーティサービス（OpenAI APIなど）は別途料金が発生する場合があります。
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ワークフロー情報
難易度
上級
ノード数18
カテゴリー-
ノードタイプ9
作成者
Adnan Tariq
@adnantariqFounder of CYBERPULSE AI — helping security teams and SMEs eliminate repetitive tasks through modular n8n automations. I build workflows for vulnerability triage, compliance reporting, threat intel, and Red/Blue/GRC ops. Book a session if you'd like custom automation for your use case. https://linkedin.com/in/adnan-tariq-4b2a1a47
外部リンク
n8n.ioで表示 →
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