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performing-ai-driven-osint-correlation

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.

internal/tools/skills/data/threat-intelligence/performing-ai-driven-osint-correlation/SKILL.md

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# 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.

## Detection Gaps & Validation

- **LLM hallucination and over-correlation:** the model may invent linkages or assert connections the raw findings do not support. Never accept a correlation > 0.8 without independent verification against >=2 sources, and manually spot-check 10–20% of all linkages.
- **Common-username false positives:** generic handles (`admin`, `test`, `info`) and high-collision names inflate Sherlock matches into bogus entity profiles. Filter these before correlation, not after.
- **Confidence-score inflation:** treat the AI's 0.0–1.0 score as a hypothesis weighted by evidence type (exact username match high, temporal proximity low) - not ground truth. Require corroborating metadata for "medium" links.