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)
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_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"}}}
},
}
}
}
