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building-vulnerability-aging-and-sla-tracking

Lets your agent track the time to fix security flaws. It helps ensure problems are resolved within deadlines.

What it does

This system helps track how long security flaws (vulnerabilities) take to be fixed. It ensures fixes are made within set deadlines based on the flaw's severity and alerts if deadlines are approaching.

When to use it

  • ✓When defining rules for fixing security flaws.
  • ✓When creating dashboards to see flaw remediation time.
  • ✓When proving that flaws are being fixed on time.

Example of what to ask your AI

“I want to see a report of which security flaws are taking too long to fix and if we are meeting our defined deadlines.”

Want more? Open the sections below.

How to use

The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.

Skill link

https://github.com/mukul975/Anthropic-Cybersecurity-Skills/blob/main/skills/building-vulnerability-aging-and-sla-tracking/SKILL.md

Ready-to-paste message

Use this skill to help me: https://github.com/mukul975/Anthropic-Cybersecurity-Skills/blob/main/skills/building-vulnerability-aging-and-sla-tracking/SKILL.md Read the SKILL.md file at that link and follow its instructions for my task.
How it works

It compares the date a flaw was discovered with the date it was fixed. If it takes longer than the allowed time for its severity, it flags it as overdue and can send alerts.

Installation

Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.

In the terminal (Claude Code, Codex, Cursor), in your project folder:

npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill building-vulnerability-aging-and-sla-tracking

Or download just the file:

curl -L https://raw.githubusercontent.com/mukul975/Anthropic-Cybersecurity-Skills/main/skills/building-vulnerability-aging-and-sla-tracking/SKILL.md -o SKILL.md
Technical content (original SKILL.md)

Original text by the author, straight from GitHub.

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.

When to Use

  • 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

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%

Workflow

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

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"])

…
FAQ

What is a vulnerability?

It's a weakness in a system or program that could be exploited by someone with bad intentions to cause harm.

What is an SLA?

SLA stands for Service Level Agreement. In security, it refers to the defined deadlines for fixing found flaws.

How does the system know the severity of a flaw?

It typically uses a score called CVSS, which classifies flaws as critical, high, medium, or low.

Details

Original description by the author

Implement a vulnerability aging dashboard and SLA tracking system that measures time-to-remediation against severity-based deadlines (e.g. 14 days critical, 30 days high, 60 days medium, 90 days low), with automated escalations and compliance metrics reporting. Use when designing SLA policies, building aging/remediation dashboards, or proving compliance with remediation timelines.

repo: mukul975/Anthropic-Cybersecurity-Skills

Popularity

GitHub stars
35k
Ranking position
#5558
License
Apache-2.0
Updated
8/31/2026

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