India faces an unprecedented digital forensics chal-
lenge stemming from its 1.4 billion population, rapidly increasing
cybercrime rates, and a severe shortage of skilled forensic profes-
sionals. Memory forensics, a crucial but technically demanding
investigative technique, currently requires hours of specialized
analysis that most law enforcement agencies cannot adequately
staff or support. This paper introduces a transformative solution:
the Volatility MCP Server that integrates the Volatility memory
forensics framework with Large Language Models via the Model
Context Protocol. Our implementation enables investigators to
perform sophisticated memory forensics through natural lan-
guage queries rather than complex command-line operations,
reducing analysis time from hours to minutes without sacrificing
analytical rigor. We provide a detailed technical implementation,
comprehensive performance evaluation, and specific application
to India’s cybersecurity challenges. Test results demonstrate a
78% reduction in analysis time for novice users and a 32%
reduction for expert analysts across standard memory foren-
sics workflows. By democratizing access to advanced forensic
capabilities, this approach directly addresses India’s critical case
backlog crisis while maintaining the forensic integrity necessary
for successful prosecutions. The paper concludes with a roadmap
for expanding this approach to other digital forensic domains.