网络安全 Automating Ioc Enrichment

Model: minimax-m3 | ¥0.20/call

网络安全Claude Opus 4.7安全审计AutomatingIoc

Automating Ioc Enrichment：网络安全 skill: automating-ioc-enrichment，适用于安全分析、取证与威胁排查场景。

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Skill Documentation

网络安全 Automating Ioc Enrichment

摘要

Automating Ioc Enrichment：网络安全 skill: automating-ioc-enrichment，适用于安全分析、取证与威胁排查场景。

> 来源: mukul975/Anthropic-Cybersecurity-Skills (18k stars) — 网络安全专业技能集

> 原文件: skills/automating-ioc-enrichment/SKILL.md

> 模型推荐: claude-opus-4-7 (安全分析深度推理)

这个 skill 是干嘛的

mukul975 整理的 100+ 个网络安全专业 skill — 覆盖渗透测试 / 取证 / 威胁情报 / 合规审计 / 云安全 / 移动安全等领域。每个 skill 对应一个具体的安全分析任务。

michael 强调"skill 要有相应的指导功能,指导用户使用",所以加了下面两节让 Agent 和用户对接。

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🤖 Agent 使用说明

1. 用户提到"分析 X 日志 / 取证 / 检测威胁 / 渗透测试 / 安全审计"时触发对应 skill

2. skill 按操作步骤执行(取证镜像 / 解析日志 / 跑威胁情报)

3. 涉及破坏性操作前必须 ask user 确认

4. 完工后跑自检

5. 区分"防御性分析" vs "恶意代码审计"

👤 用户需要做什么?

1. 告诉 Agent 你要做什么(分析日志 / 取证 / 安全审计 / 渗透测试)

2. 按 Agent 提示提供文件/镜像/日志/哈希

3. 涉及破坏性操作时明确告诉 Agent"继续"或"取消"

4. 全程 Agent 自动化,你只需提供数据 + 回答决策点

---

原 skill 内容(mukul975/Anthropic-Cybersecurity-Skills/skills/automating-ioc-enrichment/SKILL.md,截断到 12k chars)

---

name: automating-ioc-enrichment

description: 'Automates the enrichment of raw indicators of compromise with multi-source

threat intelligence context using SOAR platforms, Python pipelines, or TIP playbooks

to reduce analyst triage time and standardize enrichment outputs. Use when building

automated enrichment workflows integrated with SIEM alerts, email submission pipelines,

or bulk IOC processing from threat feeds. Activates for requests involving SOAR

enrichment, Cortex XSOAR, Splunk SOAR, TheHive, Python enrichment pipelines, or

automated IOC processing.

domain: cybersecurity

subdomain: threat-intelligence

tags:

SOAR
enrichment
IOC
Cortex-XSOAR
Splunk-SOAR
VirusTotal
automation
CTI
NIST-CSF

version: 1.0.0

author: team-cybersecurity

license: Apache-2.0

nist_csf:

ID.RA-01
ID.RA-05
DE.CM-01
DE.AE-02

mitre_attack:

T1071.001
T1583.001
T1588.001
T1590.005
T1596

---

Automating IOC Enrichment

When to Use

Use this skill when:

Building a SOAR playbook that automatically enriches SIEM alerts with threat intelligence context before routing to analysts
Creating a Python pipeline for bulk IOC enrichment from phishing email submissions
Reducing analyst mean time to triage (MTTT) by pre-populating alert context with VT, Shodan, and MISP data

**Do not use** this skill for fully automated blocking decisions without human review — enrichment automation should inform decisions, not execute blocks autonomously for high-impact actions.

Prerequisites

SOAR platform (Cortex XSOAR, Splunk SOAR, Tines, or n8n) or Python 3.9+ environment
API keys: VirusTotal, AbuseIPDB, Shodan, and at minimum one TIP (MISP or OpenCTI)
SIEM integration endpoint for alert consumption
Rate limit budgets documented per API (VT: 4/min free, 500/min enterprise)

Workflow

Step 1: Design Enrichment Pipeline Architecture

Define the enrichment flow for each IOC type:

SIEM Alert → Extract IOCs → Classify Type → Route to enrichment functions
  IP Address → AbuseIPDB + Shodan + VirusTotal IP + MISP
  Domain → VirusTotal Domain + PassiveTotal + Shodan + MISP
  URL → URLScan.io + VirusTotal URL + Google Safe Browse
  File Hash → VirusTotal Files + MalwareBazaar + MISP
→ Aggregate results → Calculate confidence score → Update alert → Notify analyst

Step 2: Implement Python Enrichment Functions

import requests
import time
from dataclasses import dataclass, field
from typing import Optional

RATE_LIMIT_DELAY = 0.25  # 4 requests/second for VT free tier

@dataclass
class EnrichmentResult:
    ioc_value: str
    ioc_type: str
    vt_malicious: int = 0
    vt_total: int = 0
    abuse_confidence: int = 0
    shodan_ports: list = field(default_factory=list)
    misp_events: list = field(default_factory=list)
    confidence_score: int = 0

def enrich_ip(ip: str, vt_key: str, abuse_key: str, shodan_key: str) -> EnrichmentResult:
    result = EnrichmentResult(ip, "ip")

    # VirusTotal IP lookup
    vt_resp = requests.get(
        f"https://www.virustotal.com/api/v3/ip_addresses/{ip}",
        headers={"x-apikey": vt_key}
    )
    if vt_resp.status_code == 200:
        stats = vt_resp.json()["data"]["attributes"]["last_analysis_stats"]
        result.vt_malicious = stats.get("malicious", 0)
        result.vt_total = sum(stats.values())

    time.sleep(RATE_LIMIT_DELAY)

    # AbuseIPDB
    abuse_resp = requests.get(
        "https://api.abuseipdb.com/api/v2/check",
        headers={"Key": abuse_key, "Accept": "application/json"},
        params={"ipAddress": ip, "maxAgeInDays": 90}
    )
    if abuse_resp.status_code == 200:
        result.abuse_confidence = abuse_resp.json()["data"]["abuseConfidenceScore"]

    # Calculate composite confidence score
    result.confidence_score = min(
        (result.vt_malicious / max(result.vt_total, 1)) * 60 +
        (result.abuse_confidence / 100) * 40, 100
    )

    return result

def enrich_hash(sha256: str, vt_key: str) -> EnrichmentResult:
    result = EnrichmentResult(sha256, "sha256")
    vt_resp = requests.get(
        f"https://www.virustotal.com/api/v3/files/{sha256}",
        headers={"x-apikey": vt_key}
    )
    if vt_resp.status_code == 200:
        stats = vt_resp.json()["data"]["attributes"]["last_analysis_stats"]
        result.vt_malicious = stats.get("malicious", 0)
        result.vt_total = sum(stats.values())
        result.confidence_score = int((result.vt_malicious / max(result.vt_total, 1)) * 100)
    return result

Step 3: Build SOAR Playbook (Cortex XSOAR)

In Cortex XSOAR, create an enrichment playbook:

1. **Trigger**: Alert created in SIEM (via webhook or polling)

2. **Extract IOCs**: Use "Extract Indicators" task with regex patterns for IP, domain, URL, hash

3. **Parallel enrichment**: Fan-out to multiple enrichment tasks simultaneously

4. **VT Enrichment**: Call `!vt-file-scan` or `!vt-ip-scan` commands

5. **AbuseIPDB check**: Call `!abuseipdb-check-ip` command

6. **MISP Lookup**: Call `!misp-search` for cross-referencing

7. **Score aggregation**: Python transform task computing composite score

8. **Conditional routing**: If score ≥70 → High Priority queue; if 40–69 → Medium; <40 → Auto-close with note

9. **Alert enrichment**: Write enrichment results to alert context for analyst view

Step 4: Handle Rate Limiting and Failures

import time
from functools import wraps

def rate_limited(max_per_second):
    min_interval = 1.0 / max_per_second
    def decorator(func):
        last_called = [0.0]
        @wraps(func)
        def wrapper(*args, **kwargs):
            elapsed = time.time() - last_called[0]
            wait = min_interval - elapsed
            if wait > 0:
                time.sleep(wait)
            result = func(*args, **kwargs)
            last_called[0] = time.time()
            return result
        return wrapper
    return decorator

def retry_on_429(max_retries=3):
    def decorator(func):
        @wraps(func)
        def wrapper(*args, **kwargs):
            for attempt in range(max_retries):
                response = func(*args, **kwargs)
                if response.status_code == 429:
                    retry_after = int(response.headers.get("Retry-After", 60))
                    time.sleep(retry_after)
                else:
                    return response
        return wrapper
    return decorator

Step 5: Metrics and Tuning

Track pipeline performance weekly:

**Enrichment latency**: Target <30 seconds from alert trigger to enriched output
**API success rate**: Target >99% (identify rate limit or outage events)
**True positive rate**: Track analyst overrides of automated confidence scores
**Cost**: Track API call volume against budget (VT Enterprise: $X per 1M lookups)

Key Concepts

| Term | Definition |

|------|-----------|

| **SOAR** | Security Orchestration, Automation, and Response — platform for automating security workflows and integrating disparate tools |

| **Enrichment Playbook** | Automated workflow sequence that adds contextual intelligence to raw security events |

| **Rate Limiting** | API provider restrictions on request frequency (e.g., VT free: 4 requests/minute); pipelines must respect these limits |

| **Composite Confidence Score** | Single score aggregating signals from multiple enrichment sources using weighted formula |

| **Fan-out Pattern** | Parallel execution of multiple enrichment queries simultaneously to minimize total enrichment latency |

Tools & Systems

**Cortex XSOAR (Palo Alto)**: Enterprise SOAR with 700+ marketplace integrations including VT, MISP, Shodan, and AbuseIPDB
**Splunk SOAR (Phantom)**: SOAR platform with Python-based playbooks; native Splunk SIEM integration
**Tines**: No-code SOAR platform with webhook-driven automation; cost-effective for smaller teams
**TheHive + Cortex**: Open-source IR/enrichment platform with observable enrichment via Cortex analyzers

Common Pitfalls

**Blocking on enrichment latency**: If enrichment takes >5 minutes, analysts start working unenriched alerts, defeating the purpose. Set timeout limits and provide partial results.
**No caching**: Querying the same IOC 50 times generates unnecessary API costs. Cache enrichment results for 24 hours by default.
**Ignoring API failures silently**: Failed enrichment calls should be logged and trigger fallback logic, not silently produce empty results that appear as clean IOCs.
**Automating blocks on enrichment score alone**: Composite scores contain false positives; require human confirmation for blocking decisions against shared infrastructure.

常见问题（FAQ）

使用「Automating Ioc」这个 skill 能解决什么问题?

本 skill 专注于Automating Ioc，网络安全 skill: automating-ioc-enrichment。它将相关流程标准化，帮助用户更快拿到可靠结果，减少重复手工操作。

什么情况下适合使用「Automating Ioc」?

当你需要在Automating Ioc Enrichment相关工作中获得稳定、可复用的产出时最适合——无论是单次任务还是纳入日常工作流，都能直接调用。

使用「Automating Ioc」前需要准备什么?

需要明确授权范围内的目标系统或样本文件，并准备隔离的分析环境（虚拟机/沙箱）。

FAQ