performing-ai-driven-osint-correlation

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Use AI and LLM-based reasoning to correlate findings across multiple OSINT sources—username enumeration, email lookups, social media profiles, domain records, breach databases, and dark-web mentions—into unified intelligence profiles with confidence scoring and link analysis.

AI & Automation 12,642 stars 1468 forks Updated today Apache-2.0

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Skill Content

# 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](https://github.com/sherlock-project/sherlock) installed (`pip install sherlock-project`) - [theHarvester](https://github.com/laramies/theHarvester) installed (`pip install theHarvester`) - [SpiderFoot](https://github.com/smicallef/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 ## Workflow ### Legal & Ethical Requirements - Obtain documented written authorization before any investigation - Establish lawful basis for data processing (law enforcement, corporate policy, etc.) - Define PII retention limits and data handling procedures - Comply with local privacy regulations (GDPR, CCPA, etc.) ### Phase 1 — Multi-Source OSINT Collection 0. **Create...

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Author
mukul975
Repository
mukul975/Anthropic-Cybersecurity-Skills
Created
3 months ago
Last Updated
today
Language
Python
License
Apache-2.0

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