incident-response-%26-digital-forensics（事件响应 %26 数字取证）

🚨 Incident Response & Digital Forensics

Overview

This skill enables Claude to assist with structured incident response operations, digital evidence collection and preservation, forensic timeline analysis, memory forensics, and comprehensive post-incident reporting. It follows NIST SP 800-61 and SANS incident handling methodology.

Prerequisites

Required

• Python 3.8+
• pyyaml, jinja2, pandas

Optional

• Volatility 3 — Memory forensics
• Autopsy / Sleuth Kit — Disk forensics
• plaso / log2timeline — Timeline generation
• KAPE — Evidence collection (Windows)
• velociraptor — Endpoint forensics

pip install pyyaml jinja2 pandas python-dateutil

Core Capabilities

1. IR Playbook Creation & Execution

When the user asks to create or follow an IR playbook:

1. Identify the incident type (ransomware, phishing, data breach, insider threat, DDoS, malware, account compromise)
2. Generate a step-by-step playbook following the PICERL framework:

- Preparation — Verify tools, access, and communication channels - Identification — Confirm the incident, scope, and severity - Containment — Short-term and long-term containment strategies - Eradication — Remove threat actors, malware, and persistence - Recovery — Restore systems and verify integrity - Lessons Learned — Post-incident review and improvement

1. Include role assignments (IR Lead, Forensics, Comms, Legal)
2. Define escalation criteria and communication templates
3. Set timeline expectations for each phase

2. Evidence Collection & Preservation

When the user asks to collect evidence:

1. Follow order of volatility (most volatile first):

- Running processes, network connections, memory - Temporary files, login sessions - Disk images, log files - Backup media, physical evidence

1. Document chain of custody for each evidence item
2. Calculate and verify cryptographic hashes
3. Create forensic images where applicable
4. Preserve log files from relevant sources
5. Generate evidence inventory manifest

3. Forensic Timeline Analysis

When the user asks to build a timeline:

1. Collect timestamps from all available sources (logs, filesystem, registry, memory)
2. Normalize timestamps to UTC
3. Correlate events across multiple data sources
4. Identify the initial compromise (patient zero)
5. Map the kill chain progression
6. Highlight critical events with context
7. Export timeline in CSV/JSON/HTML format

4. Memory Forensics

When the user asks about memory forensics:

1. Guide memory acquisition (live vs. dead analysis)
2. Profile identification for Volatility
3. Process listing and analysis (pstree, pslist, psscan)
4. Network connection extraction (netscan)
5. DLL and module analysis
6. Registry hive extraction from memory
7. Malware detection in memory artifacts
8. Code injection detection

5. Post-Incident Reporting

When the user asks for an IR report:

1. Executive summary (non-technical audience)
2. Incident timeline with visual representation
3. Scope and impact assessment
4. Root cause analysis
5. Remediation actions taken
6. Recommendations to prevent recurrence
7. Compliance notification requirements (GDPR, HIPAA, PCI-DSS)

Usage Instructions

Example Prompts

> Create an incident response playbook for a ransomware attack
> Help me collect forensic evidence from this compromised Windows server
> Build a timeline from these log files to trace the attack
> Guide me through memory forensics with Volatility on this dump
> Generate a post-incident report for management

Script Reference

evidence_collector.py

python scripts/evidence_collector.py --host 192.168.1.100 --output evidence/ --type full
python scripts/evidence_collector.py --logs /var/log/ --output evidence/ --type logs-only

timeline_builder.py

python scripts/timeline_builder.py --logs ./collected_logs/ --output timeline.csv
python scripts/timeline_builder.py --logs ./logs/ --format html --start "2024-01-15" --end "2024-01-16"

Integration Guide

• ← CSOC Automation (11): Receive triaged alerts requiring IR
• → Threat Hunting (06): Feed IOCs for environment-wide hunting
• → Malware Analysis (05): Analyze collected malware samples
• → Log Analysis (12): Deep-dive into specific log sources

References

• NIST SP 800-61 — Computer Security Incident Handling Guide
• SANS Incident Response Process
• Volatility Documentation
• The Art of Memory Forensics (Book)