Performing AI-Driven OSINT Correlation

When to Use

• You have collected raw OSINT data from multiple tools and sources but need to identify connections, contradictions, and patterns across them.
• You need to build a unified intelligence profile for a target entity (person, organization, or infrastructure) from fragmented data.
• Traditional manual correlation is too slow or error-prone for the volume of data collected.
• You want confidence-scored assessments of identity linkage across platforms rather than simple keyword matching.

Prerequisites

• Python 3.10+ with requests, json, and csv libraries
• Sherlock installed (pip install sherlock-project)
• theHarvester installed (pip install theHarvester)
• SpiderFoot 4.0+ running on localhost:5001
• Access to an LLM API (OpenAI, Anthropic, or local model via Ollama)
• Optional: Maltego CE for graph visualization of correlation results
• Optional: API keys for Shodan, VirusTotal, HaveIBeenPwned, Hunter.io