Lead Security Engineer · 2024
Phantom
INNEFU Labs (DRDO-affiliated), Ministry of Defence, India
01
The Challenge
India's Ministry of Defence oversees 50+ mission-critical government web assets — each a potential attack vector for nation-state adversaries. Security teams were drowning in manual vulnerability assessments: fragmented tooling, no centralized reporting, and scan-to-report cycles that took days instead of hours.
02
The Approach
Rather than patching together off-the-shelf scanners, I architected Phantom from the ground up as a modular, plugin-based automation framework. Django was chosen for its battle-tested ORM and admin interface, while ASGI enabled real-time asynchronous vulnerability triage. An LLM pipeline using LangChain and LangGraph was integrated for auto-triage.
03
The Solution
- Core Engine: Django 4.2 + Python 3.11 + ASGI with extensible plugin architecture
- 10+ Tool Integrations: SQLmap, ZAP Proxy, Nmap, Nuclei, Selenium Wire, Wappalyzer, AMASS, Dirsearch, Ghauri
- AI-Powered Triage: LangChain + LangGraph LLM pipeline for auto-classifying vulnerabilities
- VM Orchestration: Geo-network segmented virtual machine provisioning, scaling 10+ virtual nodes
- Auto-Generated Reporting: Real-time dashboards + Splunk SIEM integration
04
The Impact
0% Better Threat Detection
0% Scan Optimization
0x Faster Coverage
0% Report SLA Reduction
Tech Stack
Python Django ASGI LangChain LangGraph SQLmap ZAP Nmap Nuclei Splunk
“Phantom transformed national defense cybersecurity from a manual, days-long process into an AI-augmented, parallelized operation — proving that defense-grade security can be both automated and intelligent.”