Imported from mukul975/anthropic-cybersecurity-skills (skills/performing-asset-criticality-scoring-for-vulns/SKILL.md). Install upstream with npx skills add mukul975/anthropic-cybersecurity-skills --skill performing-asset-criticality-scoring-for-vulns. Copyright stays with the author (Apache-2.0).

Performing Asset Criticality Scoring for Vulns

Overview

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.

When to Use

• When conducting security assessments that involve performing asset criticality scoring for vulns
• When following incident response procedures for related security events
• When performing scheduled security testing or auditing activities
• When validating security controls through hands-on testing

Prerequisites

• Configuration Management Database (CMDB) or asset inventory
• Business Impact Analysis (BIA) data
• Data classification policy
• Network architecture documentation
• Stakeholder input from business unit owners

Core Concepts

Asset Criticality Scoring Model

Criticality Tier Definitions

Data Sensitivity Scoring

Workflow

Step 1: Define Scoring Criteria

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

Step 2: Integrate with Vulnerability Prioritization

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

Best Practices

1. Involve business stakeholders in criticality scoring; IT alone cannot assess business impact
2. Review and update criticality scores at least quarterly or when systems change roles
3. Automate scoring where possible using CMDB tags and data classification labels
4. Apply criticality tiers to vulnerability SLAs for risk-proportional remediation
5. Validate scoring against actual incident impact data to calibrate the model
6. Start with a simple 3-tier model before expanding to 5 tiers

Common Pitfalls

• Classifying all assets as "critical" which defeats the purpose of tiering
• Not updating criticality scores when systems are repurposed or decommissioned
• Using only technical factors without business context
• Applying uniform SLAs regardless of asset importance
• Not documenting the scoring methodology for audit and consistency

Related Skills

• performing-cve-prioritization-with-kev-catalog
• building-vulnerability-aging-and-sla-tracking
• performing-business-impact-analysis
• implementing-asset-management-program