building-vulnerability-aging-and-sla-tracking

Implement vulnerability aging dashboards and SLA tracking to measure remediation performance against severity-based deadlines.

954|172|Updated Mar 13, 2026
One-click install
npx skills add https://github.com/xalgord/xalgorix --skill building-vulnerability-aging-and-sla-tracking
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: building-vulnerability-aging-and-sla-tracking
Source: https://github.com/xalgord/xalgorix/tree/main/internal/tools/skills/data/vulnerability-management/building-vulnerability-aging-and-sla-tracking
Command: npx skills add https://github.com/xalgord/xalgorix --skill building-vulnerability-aging-and-sla-tracking

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas.

What problem does it solve?

Security teams often lack visibility into how long vulnerabilities remain unpatched and whether remediation meets severity-based deadlines, making it impossible to enforce accountability or report compliance to leadership.

Core Features & Use Cases

  • SLA Policy Framework: Define severity-based remediation timelines (14 days critical, 30 high, 60 medium, 90 low) with adaptive modifiers for KEV-listed CVEs, EPSS scores, and internet-facing assets.
  • Aging Calculation Engine: Python/pandas-based tracker that computes age, SLA deadlines, overdue status, MTTR, and compliance rates from vulnerability scan data.
  • Automated Escalations: Tiered escalation triggers at 50/75/100/120% of SLA elapsed, routing notifications from asset owners up to VP/CTO level.
  • Use Case: A security team feeds scan results into the aging tracker, generates overdue-by-severity KPIs, and publishes a monthly SLA compliance dashboard in Grafana or Elasticsearch for the security committee.

Quick Start

Ask the AI to build a vulnerability aging dashboard that calculates SLA compliance and overdue counts from your vulnerability scan data using the provided pandas-based tracker.

Frequently Asked Questions about building-vulnerability-aging-and-sla-tracking

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I track vulnerability SLA compliance?▼

Track SLA compliance by calculating each vulnerability's age from discovery date, comparing it against severity-based deadlines (14 days critical, 30 high, 60 medium, 90 low), and computing the percentage remediated within SLA. The provided pandas-based tracker automates this calculation.

What are standard vulnerability remediation SLA timelines?▼

Standard SLAs are 14 days for critical (CVSS 9.0-10.0), 30 days for high, 60 days for medium, and 90 days for low severity. Aggressive programs use 48 hours for critical, and CISA KEV-listed vulnerabilities should follow BOD 22-01 due dates.

How do I calculate MTTR for vulnerabilities?▼

Calculate MTTR as the average of remediation_date minus discovery_date across closed vulnerabilities. Include open overdue items in dashboards separately, since computing MTTR only on closed findings hides the aging backlog.

Should SLA deadlines adapt to asset context?▼

Yes, static SLAs miss risk context. Apply modifiers such as -50% for internet-facing assets, 48-hour override for CISA KEV-listed CVEs, -50% for EPSS above 0.7, and -25% for tier-1 crown jewel assets.

Why is my SLA compliance rate misleadingly high?▼

Compliance is often inflated because the SLA clock starts at report date instead of discovery date, or because overdue open items are excluded from metrics. Verify the timer uses discovery_date and that open overdue vulnerabilities appear in dashboards.

How do I handle vulnerabilities that cannot be patched in time?▼

Use a documented exception workflow requiring business justification, a compensating control description, and an expiry date, with CISO approval for critical and high exceptions. Avoid blanket waivers and cap extensions at 90 days with one renewal.