osint-investigation

Cross-source OSINT investigations automate cross-referencing public records to build evidence chains by normalizing and linking entities across sources like SEC EDGAR and OFAC SDN, producing machine-readable findings with traceable source-backed evidence.

Cross-source entity resolution in OSINT investigations is the process of normalizing and linking identities across scattered public records like ICIJ Offshore Leaks and OpenCorporates to reveal connections and timing patterns.

Yes, you can automate OSINT investigations using Python stdlib only, with no external dependencies required. The workflow coordinates data acquisition and entity-resolution across sources like CourtListener and GDELT.

Timing analysis across events from different public data sources is performed by coordinating records from platforms like Wayback Machine and USAspending to reveal cross-source timing patterns and correlations.

The best way to synthesize findings from multiple public records for journalism is to use a structured workflow that provides explicit source discovery, data acquisition, entity-resolution, and findings synthesis steps, emitting machine-readable findings.

OSINT entity resolution works with regulatory screening data like OFAC SDN by coordinating it with sources such as Senate lobbying and NYC ACRIS to reveal cross-source connections for regulatory screening.