[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71DMGZG5VWV8DBR2NJCJBJ6":3,"skill-same:01M71DMGZG5VWV8DBR2NJCJBJ6":99},{"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":28,"publishedAt":49,"linkOnly":50,"markdownContent":51,"markdownBody":52,"frontMatter":53,"refName":72,"commitSha":73,"skillDirectory":74,"forksCount":14,"repositoryHtmlUrl":75,"repositoryHomepageUrl":6,"ownerHtmlUrl":76,"ownerBlogUrl":6,"ownerDescription":77,"updatedAt":78,"install":79,"resources":92},"01M71DMGZG5VWV8DBR2NJCJBJ6","insider-threat-hunt",null,"Detects insider data exfiltration by analyzing DLP policy violations, file access patterns, upload volume anomalies, and off-hours activity in endpoint and cloud logs. Uses pandas for behavioral analytics and statistical baselines. Use when investigating insider threats or building user behavior analytics for data loss prevention.","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\u002Finsider\u002Fthreat\u002Fhunt\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000013","分析与可视化","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48],"hunt","insider","threat","insider threat hunt","file","security-operations","Bash","when","access","cybersecurity","use","t1059","dlp","policy","cm","for","detecting","volume","pandas","data loss prevention","2026-11-10 10:12:30",false,"---\nname: insider-threat-hunt\ndescription: Detects insider data exfiltration by analyzing DLP policy violations, file access patterns, upload volume anomalies, and off-hours activity in endpoint and cloud logs. Uses pandas for behavioral analytics and statistical baselines. Use when investigating insider threats or building user behavior analytics for data loss prevention.\ndomain: cybersecurity\nsubdomain: security-operations\ntags:\n- detecting\n- insider\n- data\n- exfiltration\nnist_csf:\n- DE.CM-01\n- RS.MA-01\n- GV.OV-01\n- DE.AE-02\nmodel: sonnet\nmaxTurns: 20\ntools: [Read, Bash, Glob, Grep]\nmitre_attack:\n- T1041\n- T1059\ncapec: []\n---\n\n# Detecting Insider Data Exfiltration via DLP\n\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\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,"tags":54,"capec":57,"model":58,"tools":59,"domain":38,"maxTurns":63,"nist_csf":64,"subdomain":34,"description":7,"mitre_attack":69},[45,30,55,56],"data","exfiltration",[],"sonnet",[60,35,61,62],"Read","Glob","Grep",20,[65,66,67,68],"DE.CM-01","RS.MA-01","GV.OV-01","DE.AE-02",[70,71],"T1041","T1059","main","a91f1293287ffa9f4f1b8f486cd6e835a1568c84","skills\u002Fidentity\u002Finsider\u002Fthreat\u002Fhunt","https:\u002F\u002Fgithub.com\u002FDCx7C5\u002Fai-marketplace","https:\u002F\u002Fgithub.com\u002FDCx7C5","#Python","2026-09-01 11:21:00",{"source":8,"skillName":5,"commands":80,"prompt":90,"zipAvailable":91},[81,84,87],{"tool":82,"command":83},"npx","npx skills add DCx7C5\u002Fai-marketplace --skill insider-threat-hunt",{"tool":85,"command":86},"bunx","bunx skills add DCx7C5\u002Fai-marketplace --skill insider-threat-hunt",{"tool":88,"command":89},"pnpm","pnpm dlx skills add DCx7C5\u002Fai-marketplace --skill insider-threat-hunt","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FDCx7C5\u002Fai-marketplace\u002Fblob\u002Fmain\u002Fskills\u002Fidentity\u002Finsider\u002Fthreat\u002Fhunt\u002FSKILL.md",true,[93],{"path":94,"resourceType":95,"resourceTypeDesc":96,"sizeBytes":97,"sourceHtmlUrl":98},"index.json","REFERENCE","参考文档",674,"https:\u002F\u002Fgithub.com\u002FDCx7C5\u002Fai-marketplace\u002Fblob\u002Fmain\u002Fskills\u002Fidentity\u002Finsider\u002Fthreat\u002Fhunt\u002Findex.json",[100,124,146,167,189,205],{"skillNo":101,"name":102,"nameZh":6,"description":103,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":104,"categories":105,"keywords":106,"publishedAt":49},"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",[],[107,108,109,110,111,112,43,113,114,115,116,117,118,119,120,121,122,38,123],"services","detect","services wmi persistence detect","wmi","persistence","de","process","activescript","event","subscriptions","commandline-consumer","repository","subscription","event-subscription","t1546.003","forensics","process-forensics",{"skillNo":125,"name":126,"nameZh":6,"description":127,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":128,"categories":129,"keywords":130,"publishedAt":49},"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",[],[131,107,132,110,133,134,135,38,35,136,137,138,139,140,112,141,142,143,144,145],"exploit","services wmi exec exploit","exec","remote","traffic","analysis","team","across","crackmapexec","native","command","lateral movement","read","lateral-movement","Impacket",{"skillNo":147,"name":148,"nameZh":6,"description":149,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":150,"categories":151,"keywords":152,"publishedAt":49},"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",[],[153,154,155,156,157,158,38,35,159,136,160,161,112,143,162,163,164,165,166,58],"artifact","registry","registry artifact extract","extract","evidence-collection","artifact-analysis","collection","analyze","uncover","software","activity","digital-forensics","windows-registry","regripper",{"skillNo":168,"name":169,"nameZh":6,"description":170,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":171,"categories":172,"keywords":173,"publishedAt":49},"01M71HACR4A8XTWTTSXK4124AA","processes-token-impersonate-detect","Detect Windows token impersonation and privilege abuse including SeImpersonatePrivilege exploitation via Potato attacks and token stealing techniques.","https:\u002F\u002Fgithub.com\u002FDCx7C5\u002Fai-marketplace\u002Fblob\u002Fmain\u002Fskills\u002Fwindows\u002Fprocesses\u002Ftoken\u002Fimpersonate\u002Fdetect\u002FSKILL.md",[],[108,174,175,176,177,178,179,43,180,38,123,181,182,183,184,185,186,187,188,112],"processes token impersonate detect","token","processes","attacks","windows","escalation","techniques","privilege-escalation","seimpersonate","token-impersonation","printspoofer","exploitation","potato","impersonation","via",{"skillNo":190,"name":191,"nameZh":6,"description":192,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":193,"categories":194,"keywords":195,"publishedAt":49},"01M71HAABSB4Z6QFSXEG00R4RK","processes-thread-injection-detect","Detect Windows thread injection techniques including CreateRemoteThread, QueueUserAPC, NtCreateThreadEx, and thread hijacking via process memory analysis.","https:\u002F\u002Fgithub.com\u002FDCx7C5\u002Fai-marketplace\u002Fblob\u002Fmain\u002Fskills\u002Fwindows\u002Fprocesses\u002Fthread\u002Finjection\u002Fdetect\u002FSKILL.md",[],[196,197,108,198,176,178,199,43,200,180,38,123,136,201,202,203,204,188,112,113],"thread","processes thread injection detect","injection","memory","process-injection","api","apc-injection","thread-injection","nt-api",{"skillNo":206,"name":207,"nameZh":6,"description":208,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":209,"categories":210,"keywords":211,"publishedAt":49},"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",[],[212,108,176,213,199,43,214,215,38,123,216,217,218,219,220,221,112,113,222,223],"processes hollow technique detect","tree","memory-forensics","process-hollow","peb","runpe","image","pe-header","in memory","with","mismatched","headers"]