[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71DFMPDEJYD8ZBZP5HXP2FH":3,"skill-same:01M71DFMPDEJYD8ZBZP5HXP2FH":71},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":39,"linkOnly":40,"markdownContent":41,"markdownBody":42,"frontMatter":43,"refName":44,"commitSha":45,"skillDirectory":46,"forksCount":14,"repositoryHtmlUrl":47,"repositoryHomepageUrl":6,"ownerHtmlUrl":48,"ownerBlogUrl":6,"ownerDescription":49,"updatedAt":50,"install":51,"resources":64},"01M71DFMPDEJYD8ZBZP5HXP2FH","identity-insider-threat-hunt",null,"- When investigating security incidents that require detecting insider data exfiltration via dlp - When building detection rules or threat hunting queries for this domain - When SOC analysts need structured procedures for this analysis type - When validating security monitoring coverage for related attack techniques","DCx7C5\u002Fai-marketplace","DCx7C5","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F42419559?v=4","01M71C01G2AZV3NTNGWAWSHJPP","DC","01M71C01GQW6G50S2BYMS7PP3E",0,"MIT","https:\u002F\u002Fgithub.com\u002FDCx7C5\u002Fai-marketplace\u002Fblob\u002Fmain\u002Fskills\u002Fidentity-insider-threat-hunt\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"hunt","insider","threat","identity","identity insider threat hunt","monitoring","exfiltration","analysis","soc","when","Rules","cybersecurity","techniques","detecting insider data exfiltration via dlp","dlp","incidents","for","domain","detecting","coverage","2026-11-10 10:12:30",false,"---\nname: identity-insider-threat-hunt\ndescription: \"- When investigating security incidents that require detecting insider data exfiltration via dlp - When building detection rules or threat hunting queries for this domain - When SOC analysts need structured procedures for this analysis type - When validating security monitoring coverage for related attack techniques\"\ndomain: cybersecurity\n---\n# Detecting Insider Data Exfiltration via DLP\n\n## When to Use\n\n- When investigating security incidents that require detecting insider data exfiltration via dlp\n- When building detection rules or threat hunting queries for this domain\n- When SOC analysts need structured procedures for this analysis type\n- When validating security monitoring coverage for related attack techniques\n\n## Prerequisites\n\n- Familiarity with security operations concepts and tools\n- Access to a test or lab environment for safe execution\n- Python 3.8+ with required dependencies installed\n- Appropriate authorization for any testing activities\n\n## Instructions\n\nAnalyze endpoint activity logs, cloud storage access, and email DLP events to detect\ndata exfiltration patterns using behavioral baselines and statistical anomaly detection.\n\n```python\nimport pandas as pd\n\ndf = pd.read_csv(\"file_activity.csv\", parse_dates=[\"timestamp\"])\n# Baseline: average daily upload volume per user\nbaseline = df.groupby([\"user\", df[\"timestamp\"].dt.date])[\"bytes_transferred\"].sum()\nuser_avg = baseline.groupby(\"user\").mean()\n\n# Alert on users exceeding 3x their baseline\ntoday = df[df[\"timestamp\"].dt.date == pd.Timestamp.today().date()]\ntoday_totals = today.groupby(\"user\")[\"bytes_transferred\"].sum()\nanomalies = today_totals[today_totals > user_avg * 3]\n```\n\nKey indicators:\n1. Upload volume exceeding 3x daily baseline\n2. Access to files outside normal scope\n3. Bulk downloads before resignation\n4. Off-hours file access patterns\n5. USB\u002Fexternal device usage spikes\n\n## Examples\n\n```python\n# Detect off-hours activity\ndf[\"hour\"] = df[\"timestamp\"].dt.hour\noff_hours = df[(df[\"hour\"] \u003C 6) | (df[\"hour\"] > 22)]\nsuspicious = off_hours.groupby(\"user\").size().sort_values(ascending=False)\n```\n","# Detecting Insider Data Exfiltration via DLP\n\n## When to Use\n\n- When investigating security incidents that require detecting insider data exfiltration via dlp\n- When building detection rules or threat hunting queries for this domain\n- When SOC analysts need structured procedures for this analysis type\n- When validating security monitoring coverage for related attack techniques\n\n## Prerequisites\n\n- Familiarity with security operations concepts and tools\n- Access to a test or lab environment for safe execution\n- Python 3.8+ with required dependencies installed\n- Appropriate authorization for any testing activities\n\n## Instructions\n\nAnalyze endpoint activity logs, cloud storage access, and email DLP events to detect\ndata exfiltration patterns using behavioral baselines and statistical anomaly detection.\n\n```python\nimport pandas as pd\n\ndf = pd.read_csv(\"file_activity.csv\", parse_dates=[\"timestamp\"])\n# Baseline: average daily upload volume per user\nbaseline = df.groupby([\"user\", df[\"timestamp\"].dt.date])[\"bytes_transferred\"].sum()\nuser_avg = baseline.groupby(\"user\").mean()\n\n# Alert on users exceeding 3x their baseline\ntoday = df[df[\"timestamp\"].dt.date == pd.Timestamp.today().date()]\ntoday_totals = today.groupby(\"user\")[\"bytes_transferred\"].sum()\nanomalies = today_totals[today_totals > user_avg * 3]\n```\n\nKey indicators:\n1. Upload volume exceeding 3x daily baseline\n2. Access to files outside normal scope\n3. Bulk downloads before resignation\n4. Off-hours file access patterns\n5. USB\u002Fexternal device usage spikes\n\n## Examples\n\n```python\n# Detect off-hours activity\ndf[\"hour\"] = df[\"timestamp\"].dt.hour\noff_hours = df[(df[\"hour\"] \u003C 6) | (df[\"hour\"] > 22)]\nsuspicious = off_hours.groupby(\"user\").size().sort_values(ascending=False)\n```\n",{"name":5,"domain":30,"description":7},"main","a91f1293287ffa9f4f1b8f486cd6e835a1568c84","skills\u002Fidentity-insider-threat-hunt","https:\u002F\u002Fgithub.com\u002FDCx7C5\u002Fai-marketplace","https:\u002F\u002Fgithub.com\u002FDCx7C5","#Python","2026-09-01 11:21:00",{"source":8,"skillName":5,"commands":52,"prompt":62,"zipAvailable":63},[53,56,59],{"tool":54,"command":55},"npx","npx skills add DCx7C5\u002Fai-marketplace --skill identity-insider-threat-hunt",{"tool":57,"command":58},"bunx","bunx skills add DCx7C5\u002Fai-marketplace --skill identity-insider-threat-hunt",{"tool":60,"command":61},"pnpm","pnpm dlx skills add DCx7C5\u002Fai-marketplace --skill identity-insider-threat-hunt","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FDCx7C5\u002Fai-marketplace\u002Fblob\u002Fmain\u002Fskills\u002Fidentity-insider-threat-hunt\u002FSKILL.md",true,[65],{"path":66,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":69,"sourceHtmlUrl":70},"index.json","REFERENCE","参考文档",683,"https:\u002F\u002Fgithub.com\u002FDCx7C5\u002Fai-marketplace\u002Fblob\u002Fmain\u002Fskills\u002Fidentity-insider-threat-hunt\u002Findex.json",[72,97,119,141,162,178],{"skillNo":73,"name":74,"nameZh":6,"description":75,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":76,"categories":77,"keywords":78,"publishedAt":39},"01M71HAPZ01KQ30RKTB0FFSQ05","services-wmi-persistence-detect","Detect WMI event subscription persistence by enumerating ActiveScriptEventConsumer, CommandLineEventConsumer, and permanent subscriptions in the WMI repository.","https:\u002F\u002Fgithub.com\u002FDCx7C5\u002Fai-marketplace\u002Fblob\u002Fmain\u002Fskills\u002Fwindows\u002Fservices\u002Fwmi\u002Fpersistence\u002Fdetect\u002FSKILL.md",[],[79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,30,96],"services","detect","services wmi persistence detect","wmi","persistence","de","cm","process","activescript","event","subscriptions","commandline-consumer","repository","subscription","event-subscription","t1546.003","forensics","process-forensics",{"skillNo":98,"name":99,"nameZh":6,"description":100,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":101,"categories":102,"keywords":103,"publishedAt":39},"01M71HAK6JTTTS229T17WJMMCG","services-wmi-exec-exploit","Perform lateral movement across Windows networks using WMI-based remote execution techniques including Impacket wmiexec.py, CrackMapExec, and native WMI commands for stealthy post-exploitation during red team engagements.","https:\u002F\u002Fgithub.com\u002FDCx7C5\u002Fai-marketplace\u002Fblob\u002Fmain\u002Fskills\u002Fwindows\u002Fservices\u002Fwmi\u002Fexec\u002Fexploit\u002FSKILL.md",[],[104,79,105,82,106,107,108,30,109,26,110,111,112,113,84,114,115,116,117,118],"exploit","services wmi exec exploit","exec","remote","traffic","Bash","team","across","crackmapexec","native","command","lateral movement","read","lateral-movement","Impacket",{"skillNo":120,"name":121,"nameZh":6,"description":122,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":123,"categories":124,"keywords":125,"publishedAt":39},"01M71HAG6MHKNQV8KDZKMT4886","registry-artifact-extract","Extract and analyze Windows Registry hives to uncover user activity, installed software, autostart entries, and evidence of system compromise.","https:\u002F\u002Fgithub.com\u002FDCx7C5\u002Fai-marketplace\u002Fblob\u002Fmain\u002Fskills\u002Fwindows\u002Fregistry\u002Fartifact\u002Fextract\u002FSKILL.md",[],[126,127,128,129,130,131,30,109,132,26,133,134,84,116,135,136,137,138,139,140],"artifact","registry","registry artifact extract","extract","evidence-collection","artifact-analysis","collection","analyze","uncover","software","activity","digital-forensics","windows-registry","regripper","sonnet",{"skillNo":142,"name":143,"nameZh":6,"description":144,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":145,"categories":146,"keywords":147,"publishedAt":39},"01M71HACR4A8XTWTTSXK4124AA","processes-token-impersonate-detect","Detect Windows token impersonation and privilege abuse including SeImpersonatePrivilege exploitation via Potato 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thread hijacking via process memory analysis.","https:\u002F\u002Fgithub.com\u002FDCx7C5\u002Fai-marketplace\u002Fblob\u002Fmain\u002Fskills\u002Fwindows\u002Fprocesses\u002Fthread\u002Finjection\u002Fdetect\u002FSKILL.md",[],[169,170,80,171,150,152,172,85,173,31,30,96,26,174,175,176,177,161,84,86],"thread","processes thread injection detect","injection","memory","process-injection","api","apc-injection","thread-injection","nt-api",{"skillNo":179,"name":180,"nameZh":6,"description":181,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":182,"categories":183,"keywords":184,"publishedAt":39},"01M71HA7H311V5G14WMX58HQSS","processes-hollow-technique-detect","Detect process hollowing (RunPE) by identifying processes with mismatched image paths between PEB and VAD tree, or unmapped PE headers in memory.","https:\u002F\u002Fgithub.com\u002FDCx7C5\u002Fai-marketplace\u002Fblob\u002Fmain\u002Fskills\u002Fwindows\u002Fprocesses\u002Fhollow\u002Ftechnique\u002Fdetect\u002FSKILL.md",[],[185,80,150,186,172,85,187,188,30,96,189,190,191,192,193,194,84,86,195,196],"processes hollow technique detect","tree","memory-forensics","process-hollow","peb","runpe","image","pe-header","in memory","with","mismatched","headers"]