investigating-insider-threat-indicators

调查内部威胁指标

何时使用

在以下情况下使用此技能：

• HR 移交一名即将离职的员工，要求在其通知期内进行监控
• DLP 告警显示大量数据下载或向个人存储位置的传输
• UEBA 检测到与同岗位基准显著偏离的异常访问模式
• 管理层报告称担心某员工访问其职责范围之外的敏感数据

不要在未获得适当法律授权的情况下使用——内部威胁调查必须在监控开始前与 HR、法务和隐私团队协调进行。

前提条件

• 法律授权和 HR 移交文件，记录调查的正当理由
• 具备 DLP、终端、电子邮件、代理和身份验证日志来源的 SIEM
• 带有策略告警的数据丢失防护（DLP）系统（Microsoft Purview、Symantec、Forcepoint）
• 终端监控能力（具备 USB/可移动媒体日志记录的 EDR）
• 提供任职状态、通知日期和访问权限的 HR 数据源
• 用于证据保全的证据保管链程序

工作流

步骤 1：确定调查范围和法律授权

在任何监控之前，确保获得适当授权：

INSIDER THREAT INVESTIGATION AUTHORIZATION
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Case ID:           IT-2024-0089
Subject:           [Employee Name] — [Department]
Authorized By:     [CISO / General Counsel]
Referral Source:   HR — Employee submitted resignation, 2-week notice
Justification:     Employee has access to trade secrets and customer PII
Scope:             Email, file access, USB, cloud storage, printing
Duration:          2024-03-15 to 2024-03-29 (notice period)
Privacy Review:    Completed — compliant with acceptable use policy

步骤 2：从 SIEM 构建活动时间线

查询调查对象的全面活动：

index=* (user="jsmith" OR src_user="jsmith" OR sender="[email protected]"
         OR SubjectUserName="jsmith")
earliest="2024-03-01" latest=now
| eval event_category = case(
    sourcetype LIKE "%dlp%", "DLP",
    sourcetype LIKE "%proxy%", "Web Access",
    sourcetype LIKE "%email%", "Email",
    sourcetype LIKE "%WinEventLog%", "Endpoint",
    sourcetype LIKE "%o365%", "Cloud",
    sourcetype LIKE "%vpn%", "VPN",
    sourcetype LIKE "%badge%", "Physical Access",
    1=1, sourcetype
  )
| stats count by event_category, sourcetype, _time
| timechart span=1d count by event_category

步骤 3：检测数据外泄指标

大量文件下载（SharePoint/OneDrive）：

index=o365 sourcetype="o365:management:activity" Operation IN ("FileDownloaded", "FileSynced")
UserId="[email protected]" earliest=-30d
| stats count AS downloads, sum(eval(if(isnotnull(FileSize), FileSize, 0))) AS total_bytes,
        dc(SourceFileName) AS unique_files
  by UserId, SiteUrl, _time
| bin _time span=1d
| eval total_gb = round(total_bytes / 1073741824, 2)
| where downloads > 50 OR total_gb > 1
| sort - total_gb

USB/可移动媒体使用：

index=sysmon EventCode=1 Computer="WORKSTATION-JSMITH"
(CommandLine="*removable*" OR CommandLine="*usb*"
 OR Image="*\\xcopy*" OR Image="*\\robocopy*")
| table _time, Computer, User, Image, CommandLine
| append [
    search index=endpoint sourcetype="endpoint:device_connect"
    user="jsmith" device_type="removable"
    | table _time, user, device_name, device_serial, action
  ]
| sort _time

基于电子邮件的外泄：

index=email sourcetype="o365:messageTrace"
SenderAddress="[email protected]"
| eval is_external = if(match(RecipientAddress, "@company\.com$"), 0, 1)
| eval has_attachment = if(isnotnull(AttachmentName), 1, 0)
| stats count AS total_emails,
        sum(is_external) AS external_emails,
        sum(has_attachment) AS with_attachments,
        sum(eval(if(is_external=1 AND has_attachment=1, 1, 0))) AS external_with_attach,
        sum(Size) AS total_size_bytes
  by SenderAddress
| eval external_attach_pct = round(external_with_attach / total_emails * 100, 1)
| eval total_size_mb = round(total_size_bytes / 1048576, 1)

云存储上传检测：

index=proxy user="jsmith"
(dest IN ("*dropbox.com", "*drive.google.com", "*onedrive.live.com",
          "*box.com", "*wetransfer.com", "*mega.nz")
 OR category="cloud-storage")
http_method=POST
| stats count AS uploads, sum(bytes_out) AS total_uploaded
  by user, dest, category
| eval uploaded_mb = round(total_uploaded / 1048576, 1)
| sort - uploaded_mb

步骤 4：分析访问模式异常

访问正常范围之外的敏感系统：

index=auth user="jsmith" action=success earliest=-30d
| stats dc(app) AS unique_apps, values(app) AS apps_accessed by user
| join user type=left [
    | inputlookup role_app_mapping.csv
    | search role="Financial Analyst"
    | stats values(authorized_app) AS authorized_apps by role
    | eval user="jsmith"
  ]
| eval unauthorized = mvfilter(NOT match(apps_accessed, mvjoin(authorized_apps, "|")))
| where isnotnull(unauthorized)
| table user, unauthorized, authorized_apps

非工作时间和周末活动：

index=* user="jsmith" earliest=-30d
| eval hour = tonumber(strftime(_time, "%H"))
| eval is_offhours = if(hour < 7 OR hour > 19, 1, 0)
| eval day = strftime(_time, "%A")
| eval is_weekend = if(day IN ("Saturday", "Sunday"), 1, 0)
| stats count AS total, sum(is_offhours) AS offhours, sum(is_weekend) AS weekend by user
| eval offhours_pct = round(offhours / total * 100, 1)
| eval weekend_pct = round(weekend / total * 100, 1)

步骤 5：与 HR 和物理安全数据关联

将活动与离职时间线进行比对：

| makeresults
| eval user="jsmith",
       resignation_date="2024-03-15",
       last_day="2024-03-29",
       access_revocation="2024-03-29 17:00"
| join user [
    search index=* user="jsmith" earliest=-90d
    | bin _time span=1d
    | stats count AS daily_events, dc(sourcetype) AS data_sources by user, _time
  ]
| eval phase = case(
    _time < relative_time(now(), "-30d"), "Normal (Pre-Resignation)",
    _time >= strptime(resignation_date, "%Y-%m-%d") AND _time <= strptime(last_day, "%Y-%m-%d"),
      "Notice Period",
    1=1, "Transition"
  )
| chart avg(daily_events) AS avg_events by phase

门禁/物理访问关联：

index=badge_access employee_id="jsmith" earliest=-30d
| stats count AS badge_events, values(door_name) AS doors_accessed,
        earliest(_time) AS first_badge, latest(_time) AS last_badge by employee_id
| eval areas = mvcount(doors_accessed)

步骤 6：保全证据并记录发现

为所有收集到的证据维护保管链：

import hashlib
import json
from datetime import datetime

evidence_log = {
    "case_id": "IT-2024-0089",
    "investigator": "soc_analyst_tier2",
    "collection_time": datetime.utcnow().isoformat(),
    "items": [
        {
            "item_id": "EV-001",
            "description": "Splunk export — all user activity 2024-03-01 to 2024-03-15",
            "file": "jsmith_activity_export.csv",
            "sha256": hashlib.sha256(open("jsmith_activity_export.csv", "rb").read()).hexdigest(),
            "collected_by": "analyst_doe",
            "collection_method": "Splunk search export"
        },
        {
            "item_id": "EV-002",
            "description": "DLP alert details — 47 policy violations",
            "file": "dlp_alerts_jsmith.json",
            "sha256": hashlib.sha256(open("dlp_alerts_jsmith.json", "rb").read()).hexdigest(),
            "collected_by": "analyst_doe",
            "collection_method": "Microsoft Purview export"
        }
    ]
}

with open(f"evidence_log_{evidence_log['case_id']}.json", "w") as f:
    json.dump(evidence_log, f, indent=2)

关键概念

术语	定义
内部威胁	拥有合法访问权限的个人将其滥用于未经授权目的所带来的风险
数据外泄	通过电子邮件、USB、云或其他渠道将数据未经授权地传输到组织外部
DLP	数据丢失防护——基于内容策略监控并阻止未经授权数据传输的技术
通知期监控	在员工从提出辞职到离开的时间窗口内对其进行加强监控
保管链	记录在案的证据处理程序，确保在可能的法律诉讼中保持取证完整性
知需违规	访问超出员工职责或当前任务所需的信息或系统

工具与系统

• Microsoft Purview（原 DLP）：数据分类和丢失防护平台，监控终端、电子邮件和云存储
• Splunk UBA：通过基于 ML 的异常检测识别内部威胁模式的用户行为分析系统
• Forcepoint Insider Threat：专门的内部威胁检测平台，具备行为指标和风险评分
• DTEX InTERCEPT：基于终端的内部威胁检测，专注于用户活动元数据收集
• Code42 Incydr：数据风险检测平台，专门监控跨终端和云的文件外泄

常见场景

• 即将离职的员工：在两周通知期内大量下载客户名单和产品路线图
• 心怀不满的员工：在负面绩效评估后，该员工访问其职责范围之外的高管薪资数据
• 承包商越权：外部顾问访问合同范围之外的系统，下载源代码
• 账户滥用：员工与未经授权的第三方共享凭据以获取竞争情报
• 破坏指标：IT 管理员在离职前创建后门账户并修改系统配置

Output Format

INSIDER THREAT INVESTIGATION REPORT — IT-2024-0089
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Subject:      jsmith (Financial Analyst, Finance Dept)
Period:       2024-03-01 to 2024-03-15
Status:       Employee resigned 2024-03-15, last day 2024-03-29

Key Findings:
  [HIGH]  3,847 files downloaded from SharePoint (12.4 GB) — 10x peer average
  [HIGH]  USB device connected 14 times during notice period (0 times prior month)
  [HIGH]  187 emails with attachments sent to personal Gmail
  [MEDIUM] After-hours activity increased 340% during notice period
  [MEDIUM] Accessed HR salary database 3 times (not authorized for role)

Timeline:
  Mar 01-14:  Normal activity baseline (avg 150 events/day)
  Mar 15:     Resignation submitted (activity spike to 890 events)
  Mar 16-17:  Weekend access — 2,100 SharePoint downloads
  Mar 18:     USB device first connected, DLP alert triggered

Evidence Collected:   4 items (SHA-256 verified, chain of custody documented)
Recommendation:       Immediate access revocation recommended
                      Evidence package prepared for Legal review