incident-response

Incident Response

Incident response skill for the full lifecycle from initial triage through forensic collection, severity declaration, and escalation routing. This is NOT threat hunting (see threat-detection) or post-incident compliance mapping (see governance/compliance-mapping) — this is about classifying, triaging, and managing declared security incidents.

Table of Contents

• [Overview](#overview)
• [Incident Triage Tool](#incident-triage-tool)
• [Incident Classification](#incident-classification)
• [Severity Framework](#severity-framework)
• [False Positive Filtering](#false-positive-filtering)
• [Forensic Evidence Collection](#forensic-evidence-collection)
• [Escalation Paths](#escalation-paths)
• [Regulatory Notification Obligations](#regulatory-notification-obligations)
• [Workflows](#workflows)
• [Anti-Patterns](#anti-patterns)
• [Cross-References](#cross-references)

Overview

What This Skill Does

This skill provides the methodology and tooling for incident triage and response — classifying security events into typed incidents, scoring severity, filtering false positives, determining escalation paths, and initiating forensic evidence collection under chain-of-custody controls.

Distinction from Other Security Skills

| Skill | Focus | Approach | |-------|-------|----------| | incident-response (this) | Active incidents | Reactive — classify, escalate, collect evidence | | threat-detection | Pre-incident hunting | Proactive — find threats before alerts fire | | cloud-security | Cloud posture assessment | Preventive — IAM, S3, network misconfiguration | | red-team | Offensive simulation | Offensive — test detection and response capability |

Prerequisites

A security event must be ingested before triage. Events can come from SIEM alerts, EDR detections, threat intel feeds, or user reports. The triage tool accepts JSON event payloads; see the input schema below.

Incident Triage Tool

The incident_triage.py tool classifies events, checks false positives, scores severity, determines escalation, and performs forensic pre-analysis.

# Classify an event from JSON file
python3 scripts/incident_triage.py --input event.json --classify --json

# Classify with false positive filtering enabled
python3 scripts/incident_triage.py --input event.json --classify --false-positive-check --json

# Force a severity level for tabletop exercises
python3 scripts/incident_triage.py --input event.json --severity sev1 --json

# Read event from stdin
echo '{"event_type": "ransomware", "host": "prod-db-01", "raw_payload": {}}' | \
  python3 scripts/incident_triage.py --classify --false-positive-check --json

Input Event Schema

{
  "event_type": "ransomware",
  "host": "prod-db-01",
  "user": "svc_backup",
  "source_ip": "10.1.2.3",
  "timestamp": "2024-01-15T14:32:00Z",
  "raw_payload": {}
}

Exit Codes

| Code | Meaning | Required Response | |------|---------|-------------------| | 0 | SEV3/SEV4 or clean | Standard ticket-based handling | | 1 | SEV2 — elevated | 1-hour bridge call, async coordination | | 2 | SEV1 — critical | Immediate 15-minute war room, all-hands |

Incident Classification

Security events are classified into 14 incident types. Classification drives default severity, MITRE technique mapping, and response SLA.

Incident Taxonomy

| Incident Type | Default Severity | MITRE Technique | Response SLA | |--------------|-----------------|-----------------|--------------| | ransomware | SEV1 | T1486 | 15 minutes | | data_exfiltration | SEV1 | T1048 | 15 minutes | | apt_intrusion | SEV1 | T1566 | 15 minutes | | supply_chain_compromise | SEV1 | T1195 | 15 minutes | | domain_controller_breach | SEV1 | T1078.002 | 15 minutes | | credential_compromise | SEV2 | T1110 | 1 hour | | lateral_movement | SEV2 | T1021 | 1 hour | | malware_infection | SEV2 | T1204 | 1 hour | | insider_threat | SEV2 | T1078 | 1 hour | | cloud_account_compromise | SEV2 | T1078.004 | 1 hour | | unauthorized_access | SEV3 | T1190 | 4 hours | | policy_violation | SEV3 | N/A | 4 hours | | phishing_attempt | SEV4 | T1566.001 | 24 hours | | security_alert | SEV4 | N/A | 24 hours |

SEV Escalation Triggers

Any of the following automatically re-declare a higher severity:

| Trigger | New Severity | |---------|-------------| | Ransomware note found | SEV1 | | Active exfiltration confirmed | SEV1 | | CloudTrail or SIEM disabled | SEV1 | | Domain controller access confirmed | SEV1 | | Second system compromised | SEV1 | | Exfiltration volume exceeds 1 GB | SEV2 minimum | | C-suite account accessed | SEV2 minimum |

Severity Framework

SEV Level Matrix

| Level | Name | Criteria | Skills Invoked | Escalation Path | |-------|------|----------|---------------|-----------------| | SEV1 | Critical | Confirmed ransomware; active PII/PHI exfiltration (>10K records); domain controller breach; defense evasion (CloudTrail disabled); supply chain compromise | All skills (parallel) | SOC Lead → CISO → CEO → Board Chair | | SEV2 | High | Confirmed unauthorized access to sensitive systems; credential compromise with elevated privileges; lateral movement confirmed; ransomware indicators without confirmed execution | triage + containment + forensics | SOC Lead → CISO | | SEV3 | Medium | Suspected unauthorized access (unconfirmed); malware detected and contained; single account compromise (no priv escalation) | triage + containment | SOC Lead → Security Manager | | SEV4 | Low | Security alert with no confirmed impact; informational indicator; policy violation with no data risk | triage only | L3 Analyst queue |

False Positive Filtering

The triage tool applies five filters before escalating to prevent false positive inflation.

False Positive Filter Types

| Filter | Description | Example Pattern | |--------|-------------|----------------| | CI/CD agent activity | Known build/deploy agents flagged as anomalies | jenkins, github-actions, circleci, gitlab-runner | | Test environment tagging | Assets tagged as non-production | test-, staging-, dev-, sandbox- | | Scheduled job patterns | Expected batch processes triggering alerts | cron, scheduled_task, batch_job, backup_ | | Whitelisted identities | Explicitly approved service accounts | svc_monitoring, svc_backup, datadog-agent | | Scanner activity | Known security scanners and vulnerability tools | nessus, qualys, rapid7, aws_inspector |

A confirmed false positive suppresses escalation and logs the suppression reason for audit purposes. Recurring false positives from the same source should be tuned out at the detection layer, not filtered repeatedly at triage.

Forensic Evidence Collection

Evidence collection follows the DFRWS six-phase framework and the principle of volatile-first acquisition.

DFRWS Six Phases

| Phase | Activity | Priority | |-------|----------|----------| | Identification | Identify what evidence exists and where | Immediate | | Preservation | Prevent modification — write-block, snapshot, legal hold | Immediate | | Collection | Acquire evidence in order of volatility | Immediate | | Examination | Technical analysis of collected evidence | Within 2 hours | | Analysis | Interpret findings in investigative context | Within 4 hours | | Presentation | Produce findings report with chain of custody | Before incident closure |

Volatile Evidence — Collect First

1. Live memory (RAM dump) — lost on reboot
2. Running processes and open network connections (netstat, ps)
3. Logged-in users and active sessions
4. System uptime and current time (for timeline anchoring)
5. Environment variables and loaded kernel modules

Chain of Custody Requirements

Every evidence item must be recorded with:

• SHA-256 hash at acquisition time
• Acquisition timestamp in UTC with timezone offset
• Tool provenance (FTK Imager, Volatility, dd, AWS CloudTrail export)
• Investigator identity
• Transfer log (who had custody and when)