Python Scripts for Vulnerability Management Automation
Real-world Security Operations Use Cases
Modern Vulnerability Management (VM) programs cannot scale with spreadsheets and manual tracking. To reduce risk, improve SLA compliance, and save operational hours, I built multiple Python-based automations covering the full VM lifecycle — from scan data ingestion to executive reporting.
Below are real, production-grade Python automation use cases used in enterprise security operations.
1- SLA Breach Alerting Automation (Python + Pandas)
Tracking vulnerability SLAs manually often leads to missed deadlines and blind spots, especially in large environments.
To solve this, I automated SLA monitoring and alerting using Python.
What the automation does
- Pulls weekly vulnerability exports from VM tools
- Calculates how many days each vulnerability has been open
- Compares results against the SLA matrix (Critical, High, Medium)
- Sends proactive alerts before SLA breach
Business impact
- Prevented critical SLA violations
- Improved visibility for application owners
- Reduced escalations and last-minute firefighting
Interview-ready summary
“I automated SLA tracking using Python and pandas. The script calculates vulnerability age, maps it against SLA thresholds, and sends email alerts when issues are nearing breach. This significantly reduced SLA misses and improved accountability.”
2- Automated Ticket Creation & Closure (ServiceNow / Jira Integration)
Manual ticket creation wastes time and often results in duplicate or stale tickets.
I built a Python integration to fully automate the ticket lifecycle.
What the automation does
- Auto-creates tickets for new Critical vulnerabilities
- Checks for duplicates using asset ID + CVE
- Updates tickets when remediation is detected in the next scan
- Automatically closes tickets with remediation evidence
Tools & techniques
- REST API integration (ServiceNow / Jira)
- Python
requestsmodule - Scan validation before closure
Business impact
- Eliminated duplicate tickets
- Improved Mean Time to Remediate (MTTR)
- Reduced manual effort for security and IT teams
Interview-ready summary
“I built a Python automation that creates tickets only for new critical vulnerabilities, avoids duplicates, and auto-closes them once remediation is confirmed. This improved MTTR and removed a lot of manual overhead.”
3- Risk-Based Vulnerability Prioritization Automation
CVSS alone does not reflect real business risk.
To address this gap, I automated risk-based prioritization using Python.
What the automation does
- Enriches vulnerability data with:
- CMDB asset criticality
- Exploit availability / threat intelligence
- Calculates a custom risk score
- Produces a weekly Top 10% high-risk vulnerability list
Why this matters
- Teams focus only on what truly matters
- Reduces noise and patch fatigue
- Aligns security actions with business risk
Interview-ready summary
“Instead of relying only on CVSS, I automated risk-based prioritization using Python by combining asset criticality and exploit data, generating a focused high-risk list for remediation teams.”
4- Executive Metrics & Weekly Reporting Automation
Leadership needs clear, consistent, audit-ready metrics, not raw scan data.
I automated weekly VM reporting using Python and pandas.
What the automation does
- Builds leadership-level metrics:
- Open vulnerabilities by severity
- SLA compliance status
- Week-on-week closure trends
- Top risky applications
- Generates Excel reports with charts
- Emails reports automatically to stakeholders
Business impact
- Saved several hours of manual reporting every week
- Improved audit readiness
- Delivered consistent executive visibility
Interview-ready summary
“I automated weekly vulnerability management reporting using Python, generating Excel reports with trends and metrics, and distributed them automatically to leadership.”
5-Auto-Export & Data Cleansing from VM Tools (Qualys / InsightVM / Tenable)
Raw vulnerability exports are noisy and inconsistent. I automated data extraction and cleansing.
What the automation does
- Pulls vulnerability data via VM tool APIs
- Cleans and normalizes data using pandas
- Removes duplicates and low-risk findings
- Splits reports by application owner or business unit
- Generates owner-wise Excel files
Key technical highlights
- API integration using
requests - Data processing with pandas
- Owner-based Excel report generation
Output examples
teamA_vulns.xlsxteamB_vulns.xlsx
Interview-ready summary
“I built a Python script that pulls VM data via API, cleans it with pandas, and generates owner-wise Excel reports every week, reducing noise and improving remediation efficiency.”
6-SLA Breach Alerts via Email / Teams / Slack
Beyond email, the automation can also push alerts to Teams or Slack, ensuring faster response from owners.
What the automation does
- Calculates vulnerability age dynamically
- Sends proactive notifications before SLA breach
- Supports multiple alerting channels
Why Python Automation Matters in Vulnerability Management
Python enables:
- Scalable VM operations
- Reduced manual effort
- Faster remediation cycles
- Better audit and compliance posture
If you are managing vulnerabilities at scale, automation is no longer optional — it’s essential.