A Novel Integrated OSINT Framework Using Advanced LLM

This research addresses a fundamental challenge in modern cybersecurity: open-source intelligence workflows remain fragmented, manual, and difficult to scale. Analysts spend significant time collecting data from disparate sources without efficient mechanisms for correlation and contextual analysis.

Research Objective

Design and implement an integrated OSINT framework that uses advanced large language models to automate intelligence gathering, attack surface mapping, and contextual threat analysis across multiple information sources.

Framework Architecture

The framework automates attack surface mapping across social media platforms and various open information sources. It links gathered intelligence with Cyber Threat Intelligence (CTI), Security Operations Center (SOC) workflows, Red Team activities, and Malware Analysis pipelines.

Key Capabilities

Research Context

This work is being conducted as part of a Master's in Information Security at NUST, Islamabad, with coursework completed and thesis currently in progress.

Related Work

This research connects to the ongoing OSINT LLM tool development project, which applies the framework's concepts to practical intelligence operations.

• Automated intelligence collection from open-source channels
• LLM-powered contextual analysis and entity correlation
• Real-time correlation across CTI, SOC, and offensive security workflows
• Automated reporting for intelligence dissemination
• Deep contextual analysis supporting threat attribution and risk assessment