From cybersecurity-skills
Builds multi-factor asset criticality scoring model (business impact, data sensitivity, regulatory scope, exposure, recoverability) to prioritize vulnerability remediation with tiered SLAs.
npx claudepluginhub mukul975/anthropic-cybersecurity-skills --plugin cybersecurity-skillsThis skill uses the workspace's default tool permissions.
Asset criticality scoring assigns a business impact rating to each IT asset so that vulnerability remediation efforts focus on systems with the greatest organizational risk. Without criticality context, a CVSS 9.0 vulnerability on a test server receives the same urgency as the same vulnerability on a payment processing database. This skill covers building a multi-factor scoring model incorporat...
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Asset criticality scoring assigns a business impact rating to each IT asset so that vulnerability remediation efforts focus on systems with the greatest organizational risk. Without criticality context, a CVSS 9.0 vulnerability on a test server receives the same urgency as the same vulnerability on a payment processing database. This skill covers building a multi-factor scoring model incorporating data sensitivity, business function dependency, regulatory scope, network exposure, and recoverability to create a 1-5 criticality tier that directly modifies vulnerability remediation SLAs.
| Factor | Weight | Score Range | Description |
|---|---|---|---|
| Business Function Impact | 25% | 1-5 | How critical is the supported business process |
| Data Sensitivity | 25% | 1-5 | Type and sensitivity of data processed/stored |
| Regulatory Scope | 15% | 1-5 | Regulatory requirements (PCI, HIPAA, SOX) |
| Network Exposure | 15% | 1-5 | Internet-facing vs internal-only |
| Recoverability | 10% | 1-5 | RTO/RPO requirements, DR capability |
| User Population | 10% | 1-5 | Number of users/customers affected |
| Tier | Score Range | Label | SLA Modifier | Examples |
|---|---|---|---|---|
| 1 | 4.5-5.0 | Crown Jewels | -50% SLA | Domain controllers, payment systems, ERP |
| 2 | 3.5-4.4 | High Value | -25% SLA | Email servers, HR systems, CI/CD |
| 3 | 2.5-3.4 | Standard | Baseline SLA | Internal apps, file servers |
| 4 | 1.5-2.4 | Low Impact | +25% SLA | Test environments, printers |
| 5 | 1.0-1.4 | Minimal | +50% SLA | Decommissioning, isolated labs |
| Score | Classification | Examples |
|---|---|---|
| 5 | Restricted/Secret | PII, PHI, payment card data, trade secrets |
| 4 | Confidential | Financial reports, HR records, source code |
| 3 | Internal | Internal documents, policies, project files |
| 2 | Semi-public | Marketing materials, press releases (draft) |
| 1 | Public | Published content, public APIs |
class AssetCriticalityScorer:
"""Multi-factor asset criticality scoring engine."""
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, "Crown Jewels", -0.50),
(3.5, 2, "High Value", -0.25),
(2.5, 3, "Standard", 0.00),
(1.5, 4, "Low Impact", 0.25),
(1.0, 5, "Minimal", 0.50),
]
def score_asset(self, asset):
"""Calculate criticality score for an asset."""
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": "Minimal", "sla_modifier": 0.50}
def adjust_vuln_sla(self, base_sla_days, asset_tier_data):
"""Adjust vulnerability SLA based on asset criticality."""
modifier = asset_tier_data["sla_modifier"]
adjusted = int(base_sla_days * (1 + modifier))
return max(1, adjusted) # Minimum 1 day SLA
def apply_criticality_to_vulns(vulns_df, asset_scores):
"""Enrich vulnerability data with asset criticality context."""
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", "Standard")
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