AGENT SKILL

implementing-soar-automation-with-phantom

Implements Security Orchestration, Automation, and Response (SOAR) workflows using Splunk SOAR (formerly Phantom) to automate alert triage, IOC enrichment, containment actions, and incident response playbooks. Use when SOC teams need to reduce manual analyst work, standardize response procedures, or integrate multiple security tools into automated workflows.

License 许可
Apache-2.0
最近更新
2026-08-31
Stars
★ 3.6w
Forks
4.4k

01 前言 Front Matter

name
implementing-soar-automation-with-phantom
tags
[ "soc", "soar", "phantom", "splunk-soar", "automation", "playbook", "orchestration", "incident-response" ]
author
mahipal
domain
cybersecurity
license
Apache-2.0
version
1.0
nist_csf
[ "DE.CM-01", "DE.AE-02", "RS.MA-01", "DE.AE-06" ]
subdomain
soc-operations
description
Implements Security Orchestration, Automation, and Response (SOAR) workflows using Splunk SOAR (formerly Phantom) to automate alert triage, IOC enrichment, containment actions, and incident response playbooks. Use when SOC teams need to reduce manual analyst work, standardize response procedures, or integrate multiple security tools into automated workflows.
mitre_attack
[ "T1078", "T1685.002", "T1685.005", "T1566" ]

02 资源文件 4

03 SKILL.md 正文

Implementing SOAR Automation with Phantom

When to Use

Use this skill when:

  • SOC teams need to automate repetitive triage and enrichment tasks for high-volume alerts
  • Manual response times exceed SLA requirements and automation can reduce MTTR
  • Multiple security tools (SIEM, EDR, firewall, TIP) need orchestrated response actions
  • Playbook standardization is required to ensure consistent analyst response across shifts

Do not use for fully autonomous containment without human approval gates — always include analyst decision points for high-impact actions like account disabling or host isolation.

Prerequisites

  • Splunk SOAR (Phantom) 6.x+ deployed with web interface access
  • App connectors configured: VirusTotal, CrowdStrike, ServiceNow, Active Directory, Splunk ES
  • Splunk ES integration for ingesting notable events as SOAR events
  • API credentials for each integrated tool stored in SOAR asset configuration
  • Python knowledge for custom playbook actions

Workflow

Step 1: Configure Asset Connections

Set up integrations with security tools via SOAR Apps:

VirusTotal Asset Configuration:

{
  "app": "VirusTotal v3",
  "asset_name": "virustotal_prod",
  "configuration": {
    "api_key": "YOUR_VT_API_KEY",
    "rate_limit": true,
    "max_requests_per_minute": 4
  },
  "product_vendor": "VirusTotal",
  "product_name": "VirusTotal"
}

CrowdStrike Falcon Asset:

{
  "app": "CrowdStrike Falcon",
  "asset_name": "crowdstrike_prod",
  "configuration": {
    "client_id": "CS_CLIENT_ID",
    "client_secret": "CS_CLIENT_SECRET",
    "base_url": "https://api.crowdstrike.com"
  }
}

Active Directory Asset:

{
  "app": "Active Directory",
  "asset_name": "ad_prod",
  "configuration": {
    "server": "dc01.company.com",
    "username": "soar_service@company.com",
    "password": "SERVICE_ACCOUNT_PASSWORD",
    "ssl": true
  }
}

Step 2: Build Phishing Triage Playbook

Create an automated phishing response playbook in Python (Phantom playbook format):

"""
Phishing Triage Automation Playbook
Trigger: New phishing email reported via Splunk ES notable or email ingestion
"""

import phantom.rules as phantom
import json

def on_start(container):
    # Extract artifacts (URLs, file hashes, sender) from the container
    artifacts = phantom.get_artifacts(container_id=container["id"])

    for artifact in artifacts:
        artifact_type = artifact.get("cef", {}).get("type", "")

        if artifact_type == "url":
            phantom.act("url reputation", targets=artifact,
                        assets=["virustotal_prod"],
                        callback=url_reputation_callback,
                        name="url_reputation")

        elif artifact_type == "hash":
            phantom.act("file reputation", targets=artifact,
                        assets=["virustotal_prod"],
                        callback=hash_reputation_callback,
                        name="file_reputation")

        elif artifact_type == "ip":
            phantom.act("ip reputation", targets=artifact,
                        assets=["virustotal_prod"],
                        callback=ip_reputation_callback,
                        name="ip_reputation")

def url_reputation_callback(action, success, container, results, handle):
    if not success:
        phantom.comment(container, "URL reputation check failed")
        return

    for result in results:
        data = result.get("data", [{}])[0]
        malicious_count = data.get("summary", {}).get("malicious", 0)
        total_engines = data.get("summary", {}).get("total_engines", 0)

        if malicious_count > 5:
            # High confidence malicious — auto-block and escalate
            phantom.act("block url", targets=result,
                        assets=["palo_alto_prod"],
                        name="block_malicious_url")

            phantom.set_severity(container, "high")
            phantom.set_status(container, "open")
            phantom.comment(container,
                f"URL flagged by {malicious_count}/{total_engines} engines. "
                f"Blocked on firewall. Escalating to Tier 2.")

            # Create ServiceNow ticket
            phantom.act("create ticket", targets=container,
                        assets=["servicenow_prod"],
                        parameters=[{
                            "short_description": f"Phishing - Malicious URL detected",
                            "urgency": "2",
                            "impact": "2"
                        }],
                        name="create_incident_ticket")

        elif malicious_count > 0:
            # Medium confidence — request analyst review
            phantom.promote(container, template="Phishing Investigation")
            phantom.comment(container,
                f"URL flagged by {malicious_count}/{total_engines} engines. "
                f"Requires analyst review.")

        else:
            # Clean — close with comment
            phantom.set_status(container, "closed")
            phantom.comment(container,
                f"URL clean: 0/{total_engines} engines flagged. Auto-closed.")

def hash_reputation_callback(action, success, container, results, handle):
    if not success:
        return

    for result in results:
        data = result.get("data", [{}])[0]
        positives = data.get("summary", {}).get("positives", 0)

        if positives > 10:
            # Known malware — quarantine and block
            phantom.act("quarantine device", targets=result,
                        assets=["crowdstrike_prod"],
                        name="isolate_endpoint")
            phantom.set_severity(container, "high")

def ip_reputation_callback(action, success, container, results, handle):
    if not success:
        return

    for result in results:
        data = result.get("data", [{}])[0]
        malicious = data.get("summary", {}).get("malicious", 0)

        if malicious > 3:
            phantom.act("block ip", targets=result,
                        assets=["palo_alto_prod"],
                        name="block_malicious_ip")

Step 3: Build Alert Enrichment Playbook

Automate enrichment for all incoming SIEM alerts:

"""
Universal Alert Enrichment Playbook
Runs on every new event to add context before analyst review
"""

import phantom.rules as phantom

def on_start(container):
    # Get all artifacts
    success, message, artifacts = phantom.get_artifacts(
        container_id=container["id"], full_data=True
    )

    ip_artifacts = [a for a in artifacts if a.get("cef", {}).get("sourceAddress")]
    domain_artifacts = [a for a in artifacts if a.get("cef", {}).get("destinationDnsDomain")]

    # Enrich IPs in parallel
    for artifact in ip_artifacts:
        ip = artifact["cef"]["sourceAddress"]

        # VirusTotal lookup
        phantom.act("ip reputation",
                    parameters=[{"ip": ip}],
                    assets=["virustotal_prod"],
                    callback=enrich_ip_callback,
                    name=f"vt_ip_{ip}")

        # GeoIP lookup
        phantom.act("geolocate ip",
                    parameters=[{"ip": ip}],
                    assets=["maxmind_prod"],
                    callback=geoip_callback,
                    name=f"geo_{ip}")

        # Whois lookup
        phantom.act("whois ip",
                    parameters=[{"ip": ip}],
                    assets=["whois_prod"],
                    name=f"whois_{ip}")

    # Enrich domains
    for artifact in domain_artifacts:
        domain = artifact["cef"]["destinationDnsDomain"]
        phantom.act("domain reputation",
                    parameters=[{"domain": domain}],
                    assets=["virustotal_prod"],
                    name=f"vt_domain_{domain}")

def enrich_ip_callback(action, success, container, results, handle):
    """Update container with enrichment data"""
    if success:
        for result in results:
            summary = result.get("summary", {})
            phantom.add_artifact(container, {
                "cef": {
                    "vt_malicious": summary.get("malicious", 0),
                    "vt_suspicious": summary.get("suspicious", 0),
                    "enrichment_source": "VirusTotal"
                },
                "label": "enrichment",
                "name": "VT IP Enrichment"
            })

Step 4: Implement Approval Gates for High-Impact Actions

Add human-in-the-loop for critical actions:

def containment_decision(action, success, container, results, handle):
    """Present analyst with containment options"""
    phantom.prompt(
        container=container,
        user="soc_tier2",
        message=(
            "Confirmed malicious activity detected.\n"
            f"Host: {container['artifacts'][0]['cef'].get('sourceAddress')}\n"
            f"Threat: {results[0]['summary'].get('threat_name')}\n\n"
            "Select containment action:"
        ),
        respond_in_mins=15,
        options=["Isolate Host", "Disable Account", "Both", "Monitor Only"],
        callback=execute_containment
    )

def execute_containment(action, success, container, results, handle):
    response = results.get("response", "Monitor Only")

    if response in ["Isolate Host", "Both"]:
        phantom.act("quarantine device",
                    parameters=[{"hostname": container["artifacts"][0]["cef"]["sourceHostName"]}],
                    assets=["crowdstrike_prod"],
                    name="isolate_host")

    if response in ["Disable Account", "Both"]:
        phantom.act("disable user",
                    parameters=[{"username": container["artifacts"][0]["cef"]["sourceUserName"]}],
                    assets=["ad_prod"],
                    name="disable_account")

    phantom.comment(container, f"Analyst approved: {response}")

Step 5: Configure Playbook Scheduling and Triggers

Set up event triggers in SOAR:

{
  "playbook_name": "phishing_triage_automation",
  "trigger": {
    "type": "event_created",
    "conditions": {
      "label": ["phishing", "notable"],
      "severity": ["high", "medium"]
    }
  },
  "active": true,
  "run_as": "automation_user"
}

Step 6: Monitor Playbook Performance

Track automation effectiveness with SOAR metrics:

# Query SOAR API for playbook execution stats
import requests

headers = {"ph-auth-token": "YOUR_SOAR_TOKEN"}
response = requests.get(
    "https://soar.company.com/rest/playbook_run",
    headers=headers,
    params={
        "page_size": 100,
        "filter": '{"status":"success"}',
        "sort": "create_time",
        "order": "desc"
    }
)
runs = response.json()["data"]

# Calculate automation metrics
total_runs = len(runs)
avg_duration = sum(r["end_time"] - r["start_time"] for r in runs) / total_runs
auto_closed = sum(1 for r in runs if r.get("auto_resolved"))
print(f"Total runs: {total_runs}")
print(f"Avg duration: {avg_duration:.1f}s")
print(f"Auto-resolved: {auto_closed}/{total_runs} ({auto_closed/total_runs*100:.0f}%)")

Key Concepts

Term Definition
SOAR Security Orchestration, Automation, and Response — platform integrating security tools with automated playbooks
Playbook Automated workflow defining sequential and parallel actions triggered by security events
Asset SOAR configuration for a connected security tool (API endpoint, credentials, connection parameters)
Container SOAR event object containing artifacts (IOCs) from an ingested alert or incident
Artifact Individual IOC or data point within a container (IP, hash, URL, domain, email)
Approval Gate Human-in-the-loop step requiring analyst decision before executing high-impact automated actions

Tools & Systems

  • Splunk SOAR (Phantom): Enterprise SOAR platform with 300+ app integrations and visual playbook editor
  • Splunk ES: SIEM platform feeding notable events into SOAR as containers for automated triage
  • CrowdStrike Falcon: EDR platform integrated via SOAR for automated host isolation and threat hunting
  • ServiceNow: ITSM platform integrated for automated incident ticket creation and tracking
  • Palo Alto NGFW: Firewall integrated for automated IP/URL blocking via SOAR playbooks

Common Scenarios

  • Phishing Triage: Auto-extract URLs/attachments, detonate in sandbox, block malicious, create ticket
  • Malware Alert Enrichment: Auto-enrich file hashes across VT/MalwareBazaar, isolate if confirmed malicious
  • Brute Force Response: Auto-check if attack succeeded, disable account if compromised, block source IP
  • Threat Intel IOC Processing: Auto-ingest TI feed IOCs, check against internal logs, create blocks for matches
  • Vulnerability Alert Response: Auto-query asset database for affected systems, create patching ticket with priority

Output Format

SOAR PLAYBOOK EXECUTION REPORT
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Playbook:     Phishing Triage Automation v2.3
Container:    SOAR-2024-08921
Trigger:      Notable event from Splunk ES (phishing)

Actions Executed:
  [1] URL Reputation (VirusTotal)     — 14/90 engines malicious    [2.1s]
  [2] IP Reputation (AbuseIPDB)       — Confidence: 85%            [1.3s]
  [3] Block URL (Palo Alto)           — Blocked on PA-5260         [0.8s]
  [4] Block IP (Palo Alto)            — Blocked on PA-5260         [0.7s]
  [5] Create Ticket (ServiceNow)      — INC0012345 created         [1.5s]
  [6] Prompt Analyst (Tier 2)         — Response: "Isolate Host"   [4m 12s]
  [7] Quarantine Device (CrowdStrike) — WORKSTATION-042 isolated   [3.2s]

Total Duration:    4m 22s (vs 35min avg manual triage)
Time Saved:        ~31 minutes
Disposition:       True Positive — Escalated to IR

04 同仓库更多 Skills

mukul975
mukul975/Anthropic-Cybersecurity-Skills

verifying-build-provenance-with-slsa-sigstore

Verifies artifact signatures and SLSA provenance using Sigstore's cosign (verify, verify-attestation, verify-blob-attestation) and slsa-verifier (verify-artifact), enforcing keyless OIDC builder identity and source repo against SLSA Build levels. Use in CI/CD before deploying artifacts, when consuming third-party attestations, establishing a SLSA Build L3 pipeline, or confirming provenance during incident response or admission control.

Skill与智能体开发
★ 3.6w
mukul975
mukul975/Anthropic-Cybersecurity-Skills

validating-tpm-measured-boot-attestation

Verifies TPM 2.0 measured-boot integrity and remote attestation with tpm2-tools -- reading PCRs (tpm2_pcrread), replaying the boot event log, generating and checking signed quotes (tpm2_quote/tpm2_checkquote), and sealing secrets to a PCR policy. Use to confirm a system booted trusted firmware/kernel for Zero Trust device posture, detect boot-chain tampering via PCR drift, or build a golden-value baseline for fleet attestation.

Skill与智能体开发
★ 3.6w
mukul975
mukul975/Anthropic-Cybersecurity-Skills

validating-backup-integrity-for-recovery

Validates backup integrity through cryptographic hash verification, automated restore testing, corruption detection, and recoverability checks to confirm backups are reliable for disaster recovery and ransomware response. Use before relying on backups for recovery, when building post-backup validation pipelines, auditing backup infrastructure for compliance, or checking immutable/air-gapped backups for silent corruption or tampering.

测试与质量Skill与智能体开发
★ 3.6w
mukul975
mukul975/Anthropic-Cybersecurity-Skills

triaging-windows-with-kape

Runs KAPE (Kroll Artifact Parser and Extractor) to collect targeted forensic artifacts (registry hives, $MFT, event logs, prefetch, browser data) via Targets and parse them with Modules wrapping Eric Zimmerman's EZ Tools (PECmd, MFTECmd, RECmd). Use during early incident containment/triage when full disk imaging is impractical but a defensible, parseable Windows artifact set is needed quickly, including at-scale remote collection.

Skill与智能体开发
★ 3.6w
mukul975
mukul975/Anthropic-Cybersecurity-Skills

triaging-vulnerabilities-with-ssvc-framework

Triages and prioritizes vulnerabilities with CISA's Stakeholder-Specific Vulnerability Categorization (SSVC) decision tree, weighing exploitation status (via the CISA KEV catalog and FIRST EPSS API), technical impact, automatability, and mission prevalence to output Track/Track*/Attend/Act decisions. Use when prioritizing vulnerability scan results (OpenVAS, Nessus, Qualys) for remediation planning beyond raw CVSS scores.

Skill与智能体开发
★ 3.6w
mukul975
mukul975/Anthropic-Cybersecurity-Skills

triaging-security-incident

Performs initial triage of security incidents using the NIST SP 800-61r3 and SANS PICERL frameworks, classifying incident type, assigning priority by business impact, and routing to the appropriate response team. Use when a SIEM/EDR alert needs human classification, concurrent alerts must be prioritized, or a user report or threat-intel IOC match requires initial incident categorization.

Skill与智能体开发
★ 3.6w