Cybersecurity-Intelligenz: Tägliche Zusammenfassungen und Top-Themen mit Gemini AI generieren

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        "inputSchema": "{\n  \"type\": \"array\",\n  \"items\": {\n    \"type\": \"object\",\n    \"properties\": {\n      \"title\": { \"type\": \"string\" },\n      \"content\": { \"type\": \"string\" },\n      \"references\": {\n        \"type\": \"array\",\n        \"items\": {\n          \"type\": \"object\",\n          \"properties\": {\n            \"link\": { \"type\": \"string\" },\n            \"website_name\": { \"type\": \"string\" },\n            \"publish_date\": { \"type\": \"string\", \"format\": \"date-time\" }\n          }\n        }\n      }\n    }\n  }\n}"
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      "id": "bd26af2e-0ffd-476b-acbe-cbfdcca91f4e",
      "name": "Virale Themen identifizieren und Zusammenfassung schreiben",
      "type": "@n8n/n8n-nodes-langchain.chainLlm",
      "onError": "continueRegularOutput",
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        "text": "={{ $json }}",
        "messages": {
          "messageValues": [
            {
              "message": "You are an intelligence analyst reviewing cyber threat intelligence reports from the past x days, formatted in a defined JSON schema. Your task is to identify 'viral' news items that describe the *same topic* and appear across multiple days. When such overlapping topics are found:\n\n* Merge them into a single entry.\n* Ensure the merged entry is mentioned on multiple across the grouped items.\n* Extract and synthesize a concise *title* and *summary* for the merged topic.\n* Aggregate all associated references under the merged entry.\n* Sort the merged topics based on newest source, descending.\n\nYou may merge items across different categories (e.g., “Nation state actors”, “Vulnerabilities”) if they clearly concern the same underlying event or threat. Do not include or report on any item that does not meet the above threshold. The objective is to surface recurring, high-signal topics for a daily digest, consolidating redundant reports into unified narratives. It is more important to have three well-processed viral topics, than many bad ones.\n"
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      "position": [
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      "parameters": {
        "width": 416,
        "height": 2016,
        "content": "## Collection\nIngest intelligence from RSS feeds, every morning on 7am."
      },
      "typeVersion": 1
    },
    {
      "id": "ab97c5ee-648b-46ac-961c-641948cf58b7",
      "name": "Notiz1",
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        "color": 3,
        "width": 416,
        "height": 2016,
        "content": "## Processing\nProcess the ingested data for further analysis by merging it into a single data block. Filter articles older than 24h and irrelevant fields."
      },
      "typeVersion": 1
    },
    {
      "id": "d57fe9a7-4b7e-4409-af19-0b8b0ac71fa5",
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        "color": 4,
        "width": 320,
        "height": 2016,
        "content": "## Analysis\nLeverage AI to deduplicate, organize, and summarize the information for a daily digest email."
      },
      "typeVersion": 1
    },
    {
      "id": "32e4c1c4-349f-42ac-ae19-44996f6962fb",
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        "content": "## Analysis\nLeverage AI to identity viral topics in reporting from the previous week and organize it into a viral topics threat brief."
      },
      "typeVersion": 1
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      "name": "Datum vor 7 Tagen",
      "type": "n8n-nodes-base.code",
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        "jsCode": "return [{\n  json: {\n    previous_days: new Date(Date.now() - 604800000).toISOString().split('T')[0]\n  }\n}];"
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      "typeVersion": 2
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        "content": "\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n## Dissemination\nDisseminate the report over email."
      },
      "typeVersion": 1
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      "id": "7155a07d-ab5e-4b79-9a81-3ea09c48b073",
      "name": "Notiz5",
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        "color": 3,
        "width": 1088,
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        "content": "## Processing\nSave the output in a database and collect the data of the last 7 days for viral topic identification. Filter out only the needed data and flatten the array for better AI consumption."
      },
      "typeVersion": 1
    },
    {
      "id": "dc30ac83-874b-4660-873b-ee9f3f5646c6",
      "name": "Notiz6",
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      "position": [
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        "color": 5,
        "width": 416,
        "height": 2016,
        "content": "## Dissemination\nDisseminate the report over email and save it in a database for future usage."
      },
      "typeVersion": 1
    },
    {
      "id": "3fda11d8-6593-4d49-a183-2ed8ad95ec40",
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        "content": "# AI Threat Intelligence: Compose Daily Digest & Viral Topics Reports \n## Process cybersecurity reports into an AI-generated daily threat intelligence digest and viral topic report.\nThis n8n workflow simplifies the process of digesting cybersecurity reports by summarizing, deduplicating, organizing, and identifying viral topics of interest into daily emails. \n\nIt will generate two types of emails:\n- A daily digest with summaries of deduplicated cybersecurity reports organized into various topics.\n- A daily viral topic report with summaries of recurring topics that have been identified over the last seven days. \n\n\n**This workflow template supports threat intelligence analysts digest the high number of cybersecurity reports they must analyse daily by decreasing the noise and tracking topics of importance with additional care, while providing customizability with regards to sources and output format.**\n\n## How it works\nThe workflow follows the threat intelligence lifecycle as labelled by the coloured notes.\n- Every morning, collect news articles from a set of RSS feeds.\n- Merge the feeds output and prepare them for LLM consumption.\n- Task an LLM with writing an intelligence briefing that summarizes, deduplicates, and organizes the topics.\n- Generate and send an email with the daily digest.\n- Collect the daily digests of the last seven days and prepare them for LLM consumption.\n- Task an LLM with writing a report that covers 'viral' topics that have appeared prominently in the news. \n- Store this report and send out over email.\n\n## How to use & customization\n- The workflow will trigger daily at 7am. \n- The workflow can be reused for other types of news as well. The RSS feeds can be swapped out and the AI prompts can easily be altered. \n- The parameters used for the viral topic identification process can easily be changed (number of previous days considered, requirements for a topic to be 'viral').\n\n## Requirements\n- The workflow leverages Gemini (free tier) for email content generation and Baserow for storing generated reports. The viral topic identification relies on the Gemini Pro model because of a higher data quantity and more complex task.\n- An SMTP email account must be provided to send the emails with. This can be through Gmail. "
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