Security Research

OSINT LLM Framework

Ongoing

AI-powered OSINT framework leveraging advanced LLMs for intelligence gathering, attack surface mapping, and threat correlation.

Problem

Traditional OSINT workflows are manual and fragmented, making it difficult to correlate intelligence across sources at scale.

What I Worked On

Designing and implementing an integrated OSINT framework that uses LLMs for automated intelligence gathering and contextual analysis.

Outcome

Framework automates attack surface mapping across social media and information sources, enabling real-time correlation with CTI, SOC, red team, and malware analysis workflows.

Case Study

Context: Master's thesis research at NUST focusing on integrating AI into OSINT operations for cybersecurity professionals.
Objective: Create a unified OSINT framework that automates intelligence collection, correlation, and reporting using advanced language models.
Approach: Designed an architecture that links gathered intelligence with CTI, SOC operations, red team activities, and malware analysis for deep contextual analysis.
Technology / Methodology: LLM-powered analysis, automated attack surface mapping, real-time correlation, automated reporting.
Outcome: In-progress research delivering a novel approach to AI-augmented open-source intelligence for security operations.