Initial commit - 611 cybersecurity skills across all subdomains

2026-09-04 23:40:51 +03:00 · 2026-02-25 10:47:44 +01:00
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+---
+name: building-vulnerability-aging-and-sla-tracking
+description: Implement a vulnerability aging dashboard and SLA tracking system to measure remediation performance against severity-based timelines and drive accountability.
+domain: cybersecurity
+subdomain: vulnerability-management
+tags: [vulnerability-management, sla-tracking, remediation-metrics, aging-report, kpi, compliance, risk-management]
+version: "1.0"
+author: mahipal
+license: MIT
+---
+# Building Vulnerability Aging and SLA Tracking
+
+## Overview
+With over 30,000 new vulnerabilities identified in 2024 (a 17% increase from the prior year), organizations must track how long vulnerabilities remain unpatched and whether remediation occurs within defined Service Level Agreements (SLAs). Vulnerability aging measures the time between discovery and remediation, while SLA tracking enforces severity-based deadlines. Industry benchmarks indicate standard SLAs of 14 days for critical, 30 days for high, 60 days for medium, and 90 days for low vulnerabilities, though more aggressive timelines (24-48 hours for actively exploited critical CVEs) are increasingly common. This skill covers designing SLA policies, building aging dashboards, implementing automated escalations, and generating compliance metrics.
+
+## Prerequisites
+- Vulnerability management platform with historical scan data
+- Asset inventory with criticality ratings
+- ITSM/ticketing system for remediation tracking
+- Reporting platform (Splunk, Elastic, Power BI, Grafana)
+- Stakeholder agreement on SLA timelines and escalation procedures
+
+## Core Concepts
+
+### Standard Vulnerability SLA Framework
+
+| Severity | CVSS Range | Standard SLA | Aggressive SLA | CISA KEV SLA |
+|----------|-----------|-------------|----------------|-------------|
+| Critical | 9.0-10.0 | 14 days | 48 hours | BOD 22-01 due date |
+| High | 7.0-8.9 | 30 days | 7 days | 14 days |
+| Medium | 4.0-6.9 | 60 days | 30 days | N/A |
+| Low | 0.1-3.9 | 90 days | 60 days | N/A |
+| Informational | 0.0 | Best effort | Best effort | N/A |
+
+### Adaptive SLA Modifiers
+
+| Factor | Modifier | Rationale |
+|--------|----------|-----------|
+| Internet-facing asset | -50% SLA | Higher exposure risk |
+| CISA KEV listed | Override to 48h | Active exploitation confirmed |
+| EPSS > 0.7 | -50% SLA | High exploitation probability |
+| Tier 1 (crown jewel) asset | -25% SLA | Maximum business impact |
+| Compensating control in place | +25% SLA | Risk partially mitigated |
+| Vendor patch unavailable | Exception with review date | Cannot remediate yet |
+
+### Key Performance Indicators (KPIs)
+
+| KPI | Formula | Target |
+|-----|---------|--------|
+| Mean Time to Remediate (MTTR) | Avg(remediation_date - discovery_date) | < 30 days overall |
+| SLA Compliance Rate | (Vulns remediated within SLA / Total vulns) * 100 | >= 90% |
+| Overdue Vulnerability Count | Count where age > SLA | Trending downward |
+| Vulnerability Aging Distribution | Count by age bucket (0-14d, 15-30d, 31-60d, 60+d) | Majority in 0-30d |
+| Remediation Velocity | Vulns closed per week | Trending upward |
+| Exception Rate | (Exceptions / Total vulns) * 100 | < 5% |
+
+## Implementation Steps
+
+### Step 1: Define SLA Policy Document
+
+```
+Vulnerability Remediation SLA Policy v1.0
+
+1. Scope: All information systems and applications
+2. Severity Classification: Based on CVSS v4.0/v3.1 base score
+3. SLA Timelines: See Standard SLA Framework table
+4. Adaptive Modifiers: Applied based on asset context
+5. Exception Process:
+   - Must be documented with business justification
+   - Requires compensating control description
+   - Maximum extension: 90 days (one renewal)
+   - CISO approval required for Critical/High exceptions
+6. Escalation Path:
+   - 50% SLA elapsed: Automated reminder to asset owner
+   - 75% SLA elapsed: Escalation to manager
+   - 100% SLA elapsed (overdue): CISO notification
+   - 120% SLA elapsed: VP/CTO escalation
+7. Metrics Reporting: Monthly to security committee
+```
+
+### Step 2: Build the Aging Calculation Engine
+
+```python
+import pandas as pd
+from datetime import datetime, timedelta
+
+class VulnerabilityAgingTracker:
+    """Track vulnerability aging and SLA compliance."""
+
+    SLA_DAYS = {
+        "Critical": 14,
+        "High": 30,
+        "Medium": 60,
+        "Low": 90,
+    }
+
+    def __init__(self, sla_overrides=None):
+        if sla_overrides:
+            self.SLA_DAYS.update(sla_overrides)
+
+    def calculate_aging(self, vulns_df):
+        """Calculate aging metrics for each vulnerability."""
+        today = datetime.now()
+
+        vulns_df["discovery_date"] = pd.to_datetime(vulns_df["discovery_date"])
+        vulns_df["remediation_date"] = pd.to_datetime(
+            vulns_df["remediation_date"], errors="coerce"
+        )
+
+        vulns_df["age_days"] = vulns_df.apply(
+            lambda row: (row["remediation_date"] - row["discovery_date"]).days
+            if pd.notna(row["remediation_date"])
+            else (today - row["discovery_date"]).days,
+            axis=1
+        )
+
+        vulns_df["sla_days"] = vulns_df["severity"].map(self.SLA_DAYS)
+        vulns_df["sla_deadline"] = vulns_df["discovery_date"] + \
+            pd.to_timedelta(vulns_df["sla_days"], unit="D")
+
+        vulns_df["is_overdue"] = vulns_df.apply(
+            lambda row: row["age_days"] > row["sla_days"]
+            if pd.isna(row["remediation_date"]) else False,
+            axis=1
+        )
+
+        vulns_df["sla_compliance"] = vulns_df.apply(
+            lambda row: row["age_days"] <= row["sla_days"]
+            if pd.notna(row["remediation_date"]) else None,
+            axis=1
+        )
+
+        vulns_df["days_overdue"] = vulns_df.apply(
+            lambda row: max(0, row["age_days"] - row["sla_days"])
+            if row["is_overdue"] else 0,
+            axis=1
+        )
+
+        vulns_df["sla_pct_elapsed"] = (
+            vulns_df["age_days"] / vulns_df["sla_days"] * 100
+        ).round(1)
+
+        return vulns_df
+
+    def generate_kpis(self, vulns_df):
+        """Generate KPI summary from aging data."""
+        open_vulns = vulns_df[vulns_df["remediation_date"].isna()]
+        closed_vulns = vulns_df[vulns_df["remediation_date"].notna()]
+
+        kpis = {
+            "total_vulnerabilities": len(vulns_df),
+            "open_vulnerabilities": len(open_vulns),
+            "closed_vulnerabilities": len(closed_vulns),
+            "overdue_count": open_vulns["is_overdue"].sum(),
+            "mttr_days": closed_vulns["age_days"].mean() if len(closed_vulns) > 0 else 0,
+            "sla_compliance_rate": (
+                closed_vulns["sla_compliance"].mean() * 100
+                if len(closed_vulns) > 0 else 0
+            ),
+        }
+
+        kpis["overdue_by_severity"] = (
+            open_vulns[open_vulns["is_overdue"]]
+            .groupby("severity")
+            .size()
+            .to_dict()
+        )
+
+        return kpis
+
+    def get_escalation_list(self, vulns_df):
+        """Get vulnerabilities requiring escalation."""
+        open_vulns = vulns_df[vulns_df["remediation_date"].isna()].copy()
+
+        escalations = []
+        for _, vuln in open_vulns.iterrows():
+            pct = vuln["sla_pct_elapsed"]
+            if pct >= 120:
+                level = "VP/CTO Escalation"
+            elif pct >= 100:
+                level = "CISO Notification"
+            elif pct >= 75:
+                level = "Manager Escalation"
+            elif pct >= 50:
+                level = "Owner Reminder"
+            else:
+                continue
+
+            escalations.append({
+                "cve_id": vuln.get("cve_id", ""),
+                "severity": vuln["severity"],
+                "age_days": vuln["age_days"],
+                "sla_days": vuln["sla_days"],
+                "days_overdue": vuln["days_overdue"],
+                "sla_pct": pct,
+                "escalation_level": level,
+                "asset": vuln.get("asset", ""),
+                "owner": vuln.get("owner", ""),
+            })
+
+        return pd.DataFrame(escalations)
+```
+
+### Step 3: Dashboard Visualization
+
+```python
+# Grafana/Kibana query examples for vulnerability aging
+
+# Age distribution histogram (Elasticsearch)
+age_distribution_query = {
+    "aggs": {
+        "age_buckets": {
+            "range": {
+                "field": "age_days",
+                "ranges": [
+                    {"key": "0-7 days", "to": 8},
+                    {"key": "8-14 days", "from": 8, "to": 15},
+                    {"key": "15-30 days", "from": 15, "to": 31},
+                    {"key": "31-60 days", "from": 31, "to": 61},
+                    {"key": "61-90 days", "from": 61, "to": 91},
+                    {"key": "90+ days", "from": 91},
+                ]
+            }
+        }
+    }
+}
+
+# SLA compliance trend (monthly)
+sla_trend_query = {
+    "aggs": {
+        "monthly": {
+            "date_histogram": {"field": "remediation_date", "interval": "month"},
+            "aggs": {
+                "within_sla": {
+                    "filter": {"script": {
+                        "source": "doc['age_days'].value <= doc['sla_days'].value"
+                    }}
+                }
+            }
+        }
+    }
+}
+```
+
+## Best Practices
+1. Start with achievable SLA targets and tighten them as processes mature
+2. Adapt SLAs based on asset criticality and threat context, not just CVSS scores
+3. Automate escalation notifications to reduce manual tracking overhead
+4. Track MTTR trends month-over-month to demonstrate improvement
+5. Build exception workflows that require documented compensating controls
+6. Report SLA compliance to executive leadership monthly for accountability
+7. Include aging metrics in security committee and board-level reporting
+8. Integrate SLA tracking with ITSM ticketing for end-to-end remediation visibility
+
+## Common Pitfalls
+- Setting unrealistic SLA targets that teams cannot meet, causing SLA fatigue
+- Not adapting SLAs for asset criticality, treating all systems equally
+- Lacking exception processes, forcing teams to either ignore SLAs or request blanket waivers
+- Measuring only open vulnerability count without considering age and SLA compliance
+- Not tracking the SLA clock from discovery date (using report date instead)
+- Failing to re-baseline SLAs as team maturity improves
+
+## Related Skills
+- implementing-vulnerability-remediation-sla
+- building-executive-vulnerability-risk-report
+- implementing-security-metrics-and-kpis
+- performing-remediation-validation-scanning
@@ -0,0 +1,28 @@
+# Vulnerability Aging and SLA Compliance Report Template
+
+## KPI Summary
+| Metric | Current Month | Prior Month | Target | Trend |
+|--------|--------------|-------------|--------|-------|
+| Total Open Vulnerabilities | [N] | [N] | Decreasing | [Up/Down] |
+| MTTR (all severities) | [N] days | [N] days | < 30 days | [Up/Down] |
+| SLA Compliance Rate | [N]% | [N]% | >= 90% | [Up/Down] |
+| Overdue Count | [N] | [N] | 0 | [Up/Down] |
+| Exception Count | [N] | [N] | < 5% | [Up/Down] |
+
+## Aging Distribution
+| Age Bucket | Critical | High | Medium | Low | Total |
+|------------|----------|------|--------|-----|-------|
+| 0-7 days | [N] | [N] | [N] | [N] | [N] |
+| 8-14 days | [N] | [N] | [N] | [N] | [N] |
+| 15-30 days | [N] | [N] | [N] | [N] | [N] |
+| 31-60 days | [N] | [N] | [N] | [N] | [N] |
+| 61-90 days | [N] | [N] | [N] | [N] | [N] |
+| 90+ days | [N] | [N] | [N] | [N] | [N] |
+
+## Escalation Summary
+| Level | Count | Top Offending Team |
+|-------|-------|--------------------|
+| Owner Reminder (50%) | [N] | [Team] |
+| Manager Escalation (75%) | [N] | [Team] |
+| CISO Notification (100%) | [N] | [Team] |
+| VP/CTO Escalation (120%+) | [N] | [Team] |
@@ -0,0 +1,27 @@
+# Standards and References - Vulnerability Aging and SLA Tracking
+
+## Industry Standards
+- **NIST SP 800-40 Rev 4**: Guide to Enterprise Patch Management Planning
+- **CIS Controls v8.1 Control 7**: Continuous Vulnerability Management
+- **PCI DSS v4.0 Req 6.3.3**: Security patches installed within one month
+- **BOD 22-01**: CISA remediation timelines for KEV vulnerabilities
+- **ISO 27001:2022 A.8.8**: Management of technical vulnerabilities
+
+## SLA Benchmark References
+- Tenable SLA Remediation: https://docs.tenable.com/cyber-exposure-studies/cyber-exposure-insurance/Content/SLARemediation.htm
+- Nucleus Security SLA Guide: https://nucleussec.com/blog/how-to-define-vulnerability-remediation-slas-shortcuts-2/
+- Phoenix Security SLA Framework: https://phoenix.security/vulnerability-timelines-sla-measurement-and-prioritization/
+
+## Industry SLA Benchmarks
+| Source | Critical | High | Medium | Low |
+|--------|----------|------|--------|-----|
+| CISA BOD 22-01 | 2 weeks | N/A | N/A | N/A |
+| PCI DSS | 30 days | 30 days | 90 days | 90 days |
+| Industry Average | 14 days | 30 days | 60 days | 90 days |
+| Aggressive Target | 48 hours | 7 days | 30 days | 60 days |
+
+## Vulnerability Statistics (2024)
+- Total new CVEs published: 30,000+
+- Year-over-year increase: 17%
+- Average MTTR across industries: ~60 days
+- Top-performing organizations MTTR: < 15 days for critical
@@ -0,0 +1,35 @@
+# Workflows - Vulnerability Aging and SLA Tracking
+
+## Workflow 1: SLA Lifecycle
+
+```
+┌──────────────────┐     ┌──────────────────┐     ┌──────────────────┐
+│ Vulnerability    │────>│ Assign Severity  │────>│ Calculate SLA    │
+│ Discovered       │     │ + Asset Context  │     │ Deadline         │
+└──────────────────┘     └──────────────────┘     └──────────────────┘
+        │                                                  │
+        v                                                  v
+┌──────────────────┐     ┌──────────────────┐     ┌──────────────────┐
+│ Create Ticket    │────>│ Monitor Aging    │────>│ Trigger          │
+│ (ITSM)           │     │ (Daily)          │     │ Escalations      │
+└──────────────────┘     └──────────────────┘     └──────────────────┘
+```
+
+## Workflow 2: Escalation Ladder
+
+```
+SLA % Elapsed:
+    50%  ──> Email reminder to asset owner
+    75%  ──> Escalation to owner's manager
+    100% ──> CISO notification, marked overdue
+    120% ──> VP/CTO escalation, exception required
+```
+
+## Workflow 3: Monthly Reporting Cycle
+
+```
+Week 1: Collect scan data and aging metrics
+Week 2: Generate KPI dashboard
+Week 3: Present to security committee
+Week 4: Action items assigned, SLA adjustments if needed
+```
@@ -0,0 +1,176 @@
+#!/usr/bin/env python3
+"""
+Vulnerability Aging and SLA Tracking Engine
+
+Calculates vulnerability aging, SLA compliance, and generates
+escalation reports and KPI dashboards.
+
+Requirements:
+    pip install pandas
+
+Usage:
+    python process.py analyze --csv vulns.csv --output aging_report.csv
+    python process.py kpis --csv vulns.csv
+    python process.py escalations --csv vulns.csv --output escalations.csv
+"""
+
+import argparse
+import sys
+from datetime import datetime, timedelta
+
+import pandas as pd
+
+
+SLA_DAYS = {
+    "Critical": 14,
+    "High": 30,
+    "Medium": 60,
+    "Low": 90,
+}
+
+
+def calculate_aging(df, sla_config=None):
+    """Add aging and SLA columns to vulnerability dataframe."""
+    sla = sla_config or SLA_DAYS
+    today = pd.Timestamp.now()
+
+    df["discovery_date"] = pd.to_datetime(df["discovery_date"])
+    df["remediation_date"] = pd.to_datetime(df["remediation_date"], errors="coerce")
+
+    df["age_days"] = df.apply(
+        lambda r: (r["remediation_date"] - r["discovery_date"]).days
+        if pd.notna(r["remediation_date"])
+        else (today - r["discovery_date"]).days,
+        axis=1
+    )
+
+    df["sla_days"] = df["severity"].map(sla).fillna(90).astype(int)
+    df["sla_deadline"] = df["discovery_date"] + pd.to_timedelta(df["sla_days"], unit="D")
+    df["is_open"] = df["remediation_date"].isna()
+    df["is_overdue"] = df["is_open"] & (df["age_days"] > df["sla_days"])
+    df["days_overdue"] = df.apply(
+        lambda r: max(0, r["age_days"] - r["sla_days"]) if r["is_overdue"] else 0,
+        axis=1
+    )
+    df["sla_pct_elapsed"] = (df["age_days"] / df["sla_days"] * 100).round(1)
+    df["within_sla"] = df.apply(
+        lambda r: r["age_days"] <= r["sla_days"]
+        if pd.notna(r["remediation_date"]) else None,
+        axis=1
+    )
+
+    return df
+
+
+def generate_kpis(df):
+    """Generate KPI summary."""
+    open_df = df[df["is_open"]]
+    closed_df = df[~df["is_open"]]
+
+    print(f"\n{'=' * 60}")
+    print("VULNERABILITY AGING KPI REPORT")
+    print(f"{'=' * 60}")
+    print(f"Report Date:           {datetime.now().strftime('%Y-%m-%d')}")
+    print(f"Total Vulnerabilities: {len(df)}")
+    print(f"Open:                  {len(open_df)}")
+    print(f"Closed:                {len(closed_df)}")
+    print(f"Overdue:               {open_df['is_overdue'].sum()}")
+
+    if len(closed_df) > 0:
+        mttr = closed_df["age_days"].mean()
+        sla_rate = closed_df["within_sla"].mean() * 100
+        print(f"\nMTTR (all):            {mttr:.1f} days")
+        print(f"SLA Compliance Rate:   {sla_rate:.1f}%")
+
+        for sev in ["Critical", "High", "Medium", "Low"]:
+            sev_df = closed_df[closed_df["severity"] == sev]
+            if len(sev_df) > 0:
+                print(f"  {sev} MTTR: {sev_df['age_days'].mean():.1f}d "
+                      f"| SLA: {sev_df['within_sla'].mean() * 100:.1f}%")
+
+    print(f"\nOpen Vulnerabilities by Age:")
+    bins = [0, 7, 14, 30, 60, 90, float("inf")]
+    labels = ["0-7d", "8-14d", "15-30d", "31-60d", "61-90d", "90+d"]
+    if len(open_df) > 0:
+        open_df = open_df.copy()
+        open_df["age_bucket"] = pd.cut(open_df["age_days"], bins=bins, labels=labels)
+        print(open_df["age_bucket"].value_counts().sort_index().to_string())
+
+    print(f"\nOverdue by Severity:")
+    overdue = open_df[open_df["is_overdue"]]
+    if len(overdue) > 0:
+        print(overdue.groupby("severity")["days_overdue"].agg(["count", "mean", "max"]).to_string())
+
+
+def generate_escalations(df):
+    """Generate escalation list."""
+    open_df = df[df["is_open"]].copy()
+    escalations = []
+
+    for _, row in open_df.iterrows():
+        pct = row["sla_pct_elapsed"]
+        if pct >= 120:
+            level = "VP/CTO Escalation"
+        elif pct >= 100:
+            level = "CISO Notification"
+        elif pct >= 75:
+            level = "Manager Escalation"
+        elif pct >= 50:
+            level = "Owner Reminder"
+        else:
+            continue
+
+        escalations.append({
+            "cve_id": row.get("cve_id", ""),
+            "severity": row["severity"],
+            "asset": row.get("asset", ""),
+            "owner": row.get("owner", ""),
+            "age_days": row["age_days"],
+            "sla_days": row["sla_days"],
+            "days_overdue": row["days_overdue"],
+            "sla_pct": pct,
+            "escalation_level": level,
+        })
+
+    return pd.DataFrame(escalations).sort_values("sla_pct", ascending=False)
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Vulnerability Aging and SLA Tracker")
+    subparsers = parser.add_subparsers(dest="command")
+
+    analyze_p = subparsers.add_parser("analyze", help="Calculate aging metrics")
+    analyze_p.add_argument("--csv", required=True)
+    analyze_p.add_argument("--output", default="aging_report.csv")
+
+    subparsers.add_parser("kpis", help="Generate KPI summary").add_argument("--csv", required=True)
+
+    esc_p = subparsers.add_parser("escalations", help="Generate escalation list")
+    esc_p.add_argument("--csv", required=True)
+    esc_p.add_argument("--output", default="escalations.csv")
+
+    args = parser.parse_args()
+
+    if not args.command:
+        parser.print_help()
+        sys.exit(1)
+
+    df = pd.read_csv(args.csv)
+    df = calculate_aging(df)
+
+    if args.command == "analyze":
+        df.to_csv(args.output, index=False)
+        print(f"[+] Aging report saved to {args.output}")
+        generate_kpis(df)
+    elif args.command == "kpis":
+        generate_kpis(df)
+    elif args.command == "escalations":
+        esc_df = generate_escalations(df)
+        esc_df.to_csv(args.output, index=False)
+        print(f"[+] {len(esc_df)} escalations saved to {args.output}")
+        if len(esc_df) > 0:
+            print(esc_df["escalation_level"].value_counts().to_string())
+
+
+if __name__ == "__main__":
+    main()