关于 conducting-post-incident-lessons-learned
用于在事件解决、演练或险情后开展复盘的安全技能。它引导团队进行无责分析、统计响应指标、跟踪改进事项,并更新预案和检测规则。
主要功能
- 01组织结构化复盘会议
- 02开展根因分析
- 03统计 MTTD、MTTC 和 MTTR
- 04跟踪改进事项
- 05更新预案和检测规则
适用场景
- 01复盘已解决的安全事件
- 02总结桌面演练经验
- 03分析反复出现的事件趋势
安装
复制下面的命令,在终端中运行。
$
npx skills add https:/ / github.com/ mukul975/ anthropic-cybersecurity-skills --skill conducting-post-incident-lessons-learned安装后在 Claude Code 中输入/conducting-post-incident-lessons-learned确认已加载。
包含的文件
除 SKILL.md 外,安装时一起下载的参考资料和脚本。
LICENSEassets/template.mdreferences/api-reference.mdstandards.mdworkflows.mdscripts/agent.pyprocess.py详细介绍
开展事件后经验教训总结
何时使用
- 在任何安全事件已完全解决且恢复完成后
- 在桌面演练或 IR 模拟之后
- 在发生重大未遂事件之后
- 每季度审查累积的事件趋势时
- 当 IR 手册需要根据实际经验更新时
前置条件
- 事件已完全解决(遏制、根除、恢复均已完成)
- 已收集事件时间线和文档
- 所有事件响应人员均可参加复盘会议
- 有用于协作讨论的会议空间
- 有事件工单系统数据用于指标分析
工作流
第 1 步:收集事件数据
# Export incident timeline from ticketing system
curl -s "https://thehive.local/api/v1/case/$CASE_ID/timeline" \
-H "Authorization: Bearer $THEHIVE_API_KEY" | jq '.' > incident_timeline.json
# Extract detection and response metrics from SIEM
index=notable incident_id="IR-2024-042"
| stats min(_time) as first_alert, max(_time) as last_alert,
count as total_alerts, dc(src) as unique_sources
# Compile all responder actions and timestamps
grep -E "timestamp|action|analyst" /var/log/ir/IR-2024-042/*.json | \
python3 -m json.tool > compiled_actions.json
第 2 步:召开无责事后复盘会议
Structured Agenda (90 minutes):
1. Incident summary (5 min) - Factual overview
2. Timeline walkthrough (20 min) - Chronological events
3. What worked well (15 min) - Positive outcomes
4. What needs improvement (15 min) - Gaps and failures
5. Root cause analysis (15 min) - 5 Whys or fishbone
6. Action items (10 min) - Specific improvements with owners
7. Playbook updates (10 min) - Changes to IR procedures
Blameless Principles:
- Focus on systems and processes, not individuals
- Assume best intentions with available information
- Seek to understand, not to blame
第 3 步:执行根本原因分析
# 5 Whys analysis example:
# Why 1: Why did ransomware encrypt production servers?
# Answer: Attacker had domain admin credentials
# Why 2: Why did attacker have domain admin credentials?
# Answer: Kerberoasted a service account and cracked it
# Why 3: Why was the service account password crackable?
# Answer: Used a 12-character dictionary-based password
# Why 4: Why was the service account password weak?
# Answer: No enforcement of service account password policy
# Why 5: Why was there no service account password policy?
# Answer: PAM was not implemented for service accounts
# ROOT CAUSE: Lack of privileged access management
第 4 步:计算响应指标
from datetime import datetime
events = {
'compromise': '2024-01-10 14:00:00',
'detection': '2024-01-15 08:30:00',
'triage': '2024-01-15 08:45:00',
'containment': '2024-01-15 09:30:00',
'eradication': '2024-01-16 14:00:00',
'recovery': '2024-01-18 16:00:00',
'closure': '2024-01-25 10:00:00',
}
fmt = '%Y-%m-%d %H:%M:%S'
times = {k: datetime.strptime(v, fmt) for k, v in events.items()}
print(f"Dwell Time: {times['detection'] - times['compromise']}")
print(f"MTTD: {times['triage'] - times['detection']}")
print(f"MTTC: {times['containment'] - times['detection']}")
print(f"MTTR: {times['recovery'] - times['eradication']}")
print(f"Total Duration: {times['closure'] - times['detection']}")
第 5 步:记录发现并创建行动项
# Create tracked action items in project management
curl -X POST "https://jira.local/rest/api/2/issue" \
-H "Authorization: Bearer $JIRA_TOKEN" \
-H "Content-Type: application/json" \
-d '{
"fields": {
"project": {"key": "SEC"},
"summary": "Implement PAM for service accounts (IR-2024-042)",
"issuetype": {"name": "Task"},
"priority": {"name": "High"},
"assignee": {"name": "security_engineer"},
"duedate": "2024-03-15"
}
}'
第 6 步:更新手册和检测规则
# New Sigma detection rule based on incident learnings
title: Kerberoasting Activity Detected
status: stable
description: Detects Kerberoasting based on IR-2024-042 lessons
logsource:
product: windows
service: security
detection:
selection:
EventID: 4769
TicketEncryptionType: '0x17'
condition: selection
level: high
tags:
- attack.credential_access
- attack.t1558.003
关键概念
| 概念 | 描述 |
|---|---|
| 无责事后复盘 | 审查事件时关注系统,而非归咎于个人 |
| 根本原因分析 | 识别事件发生的根本原因 |
| 5 Why | 通过反复提问找到根本原因的技术 |
| MTTD | 平均检测时间 - 从失陷到检测的时间 |
| MTTC | 平均遏制时间 - 从检测到遏制的时间 |
| MTTR | 平均恢复时间 - 从根除到完全恢复的时间 |
| 持续改进 | 基于真实事件数据迭代 IR 流程 |
工具与系统
| 工具 | 用途 |
|---|---|
| TheHive/ServiceNow | 事件时间线和文档 |
| Jira/Azure DevOps | 行动项跟踪 |
| Confluence/SharePoint | 经验教训文档 |
| Splunk/Elastic | 事件指标和检测改进 |
| Sigma | 检测规则开发 |
常见场景
- 勒索软件事后复盘:审查从初始访问到加密的整个杀伤链。识别检测缺口和备份故障。
- 钓鱼活动复盘:分析用户为何点击、邮件过滤器为何漏掉,以及如何改进培训。
- 云配置错误事件:审查 IaC 流水线、CSPM 覆盖范围和变更管理流程。
- 内部威胁复盘:检查 DLP 有效性、访问控制缺口和用户监控能力。
- 第三方泄露影响:审查供应商风险评估流程和数据共享协议。
输出格式
- 事件后复盘会议纪要
- 根本原因分析文档
- 事件指标报告(MTTD、MTTC、MTTR)
- 带负责人和截止日期的行动项列表
- 更新后的 IR 手册和检测规则
- 面向管理层的执行摘要
评论
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