Develops multi-factor asset criticality scoring model to prioritize vulnerabilities by business impact, data sensitivity, regulatory scope, and recoverability. Includes Python scorer and SLA adjuster.
npx claudepluginhub killvxk/cybersecurity-skills-zhThis skill uses the workspace's default tool permissions.
资产关键性评分（Asset Criticality Scoring）为每个 IT 资产分配业务影响评级，使漏洞修复工作集中于对组织风险最大的系统。没有关键性背景，测试服务器上的 CVSS 9.0 漏洞与支付处理数据库上的相同漏洞会得到同等紧急处理。本技能涵盖构建多因素评分模型，纳入数据敏感性、业务功能依赖性、监管范围、网络暴露和可恢复性，创建 1-5 关键性等级，直接调整漏洞修复 SLA。
Develops multi-factor asset criticality scoring model to prioritize vulnerabilities by business impact, data sensitivity, operational importance, and recoverability. For vulnerability management workflows.
Builds multi-factor asset criticality scoring model (business impact, data sensitivity, regulatory scope, exposure, recoverability) to prioritize vulnerability remediation with tiered SLAs.
Prioritizes vulnerabilities using CVSS v4.0 scoring, vector interpretation, EPSS, and CISA KEV for remediation planning. Useful for vulnerability management workflows.
Share bugs, ideas, or general feedback.
资产关键性评分（Asset Criticality Scoring）为每个 IT 资产分配业务影响评级，使漏洞修复工作集中于对组织风险最大的系统。没有关键性背景，测试服务器上的 CVSS 9.0 漏洞与支付处理数据库上的相同漏洞会得到同等紧急处理。本技能涵盖构建多因素评分模型，纳入数据敏感性、业务功能依赖性、监管范围、网络暴露和可恢复性，创建 1-5 关键性等级，直接调整漏洞修复 SLA。
| 因素 | 权重 | 分数范围 | 描述 |
|---|---|---|---|
| 业务功能影响 | 25% | 1-5 | 所支撑业务流程的关键程度 |
| 数据敏感性 | 25% | 1-5 | 处理/存储数据的类型和敏感性 |
| 监管范围 | 15% | 1-5 | 监管要求（PCI、HIPAA、SOX） |
| 网络暴露 | 15% | 1-5 | 面向互联网与仅内部使用 |
| 可恢复性 | 10% | 1-5 | RTO/RPO 要求、灾备能力 |
| 用户群体 | 10% | 1-5 | 受影响的用户/客户数量 |
| 等级 | 分数范围 | 标签 | SLA 调整 | 示例 |
|---|---|---|---|---|
| 1 | 4.5-5.0 | 皇冠宝石 | -50% SLA | 域控制器、支付系统、ERP |
| 2 | 3.5-4.4 | 高价值 | -25% SLA | 邮件服务器、HR 系统、CI/CD |
| 3 | 2.5-3.4 | 标准 | 基线 SLA | 内部应用、文件服务器 |
| 4 | 1.5-2.4 | 低影响 | +25% SLA | 测试环境、打印机 |
| 5 | 1.0-1.4 | 最小 | +50% SLA | 下线中、隔离实验室 |
| 分数 | 分类 | 示例 |
|---|---|---|
| 5 | 受限/机密 | PII、PHI、支付卡数据、商业机密 |
| 4 | 保密 | 财务报告、HR 记录、源代码 |
| 3 | 内部 | 内部文件、政策、项目文件 |
| 2 | 半公开 | 营销材料、新闻稿（草稿） |
| 1 | 公开 | 已发布内容、公开 API |
class AssetCriticalityScorer:
"""多因素资产关键性评分引擎。"""
WEIGHTS = {
"business_function": 0.25,
"data_sensitivity": 0.25,
"regulatory_scope": 0.15,
"network_exposure": 0.15,
"recoverability": 0.10,
"user_population": 0.10,
}
TIER_THRESHOLDS = [
(4.5, 1, "皇冠宝石", -0.50),
(3.5, 2, "高价值", -0.25),
(2.5, 3, "标准", 0.00),
(1.5, 4, "低影响", 0.25),
(1.0, 5, "最小", 0.50),
]
def score_asset(self, asset):
"""计算资产的关键性分数。"""
weighted_score = sum(
asset.get(factor, 3) * weight
for factor, weight in self.WEIGHTS.items()
)
score = round(weighted_score, 2)
for threshold, tier, label, sla_mod in self.TIER_THRESHOLDS:
if score >= threshold:
return {
"score": score,
"tier": tier,
"label": label,
"sla_modifier": sla_mod,
}
return {"score": score, "tier": 5, "label": "最小", "sla_modifier": 0.50}
def adjust_vuln_sla(self, base_sla_days, asset_tier_data):
"""根据资产关键性调整漏洞 SLA。"""
modifier = asset_tier_data["sla_modifier"]
adjusted = int(base_sla_days * (1 + modifier))
return max(1, adjusted) # 最短 1 天 SLA
def apply_criticality_to_vulns(vulns_df, asset_scores):
"""用资产关键性背景丰富漏洞数据。"""
for idx, vuln in vulns_df.iterrows():
asset_id = vuln.get("asset_id", "")
asset_data = asset_scores.get(asset_id, {"tier": 3, "sla_modifier": 0})
vulns_df.at[idx, "asset_tier"] = asset_data["tier"]
vulns_df.at[idx, "asset_label"] = asset_data.get("label", "标准")
base_sla = get_base_sla(vuln["severity"])
adjusted_sla = int(base_sla * (1 + asset_data["sla_modifier"]))
vulns_df.at[idx, "adjusted_sla_days"] = max(1, adjusted_sla)
return vulns_df