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Implement a vulnerability aging dashboard and SLA tracking system to measure remediation performance against severity-based timelines and drive accountability. Use when implementing a vulnerability aging dashboard and sla tracking system to.
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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. Use when implementing a vulnerability aging dashboard and sla tracking system to. |
| domain | cybersecurity |
| subdomain | vulnerability-management |
| tags | ["vulnerability-management","sla-tracking","remediation-metrics","aging-report","kpi","compliance","risk-management"] |
| version | 1.0 |
| author | oyi77 |
| license | Apache-2.0 |
| nist_csf | ["ID.RA-01","ID.RA-02","ID.IM-02","ID.RA-06"] |
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.
| Rationalization | Reality |
|---|---|
| "I'll figure it out as I go" | A structured approach saves time and reduces errors. Follow the workflow in this skill rather than improvising. |
| "I already know this topic" | Familiarity breeds shortcuts. Use the checklist to verify you haven't missed critical steps. |
| "This doesn't apply to my situation" | The patterns here generalize across contexts. Adapt, don't skip — the underlying principles hold. |
| "One more tool will fix it" | Adding complexity rarely solves process gaps. Master the core workflow first. |
Trigger phrases:
"building vulnerability aging and sla tracking"
"Implement a vulnerability aging dashboard and SLA tracking system to measure rem"
When deploying or configuring building vulnerability aging and sla tracking capabilities in your environment
When establishing security controls aligned to compliance requirements
When building or improving security architecture for this domain
When conducting security assessments that require this implementation
This section covers core concepts for building vulnerability aging and sla tracking.
| 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 |
| 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 |
| 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% |
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
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)
# 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"
}}
}
}
}
}
}