osint-investigation

Cross-referencing public records for entity resolution involves fetching data from SEC EDGAR, OFAC SDN, ICIJ Offshore Leaks, and OpenCorporates, then normalizing entities and building findings JSON with traceable evidence chains.

Yes, you can compile OSINT evidence chains from SEC EDGAR and USAspending by running Python stdlib scripts that fetch data, normalize entities, and cross-link findings to support due diligence and risk screening.

Available public-record sources for investigative journalism include SEC EDGAR, USAspending, Senate lobbying records, OFAC SDN, ICIJ Offshore Leaks, NYC ACRIS, OpenCorporates, CourtListener, Wayback Machine, Wikipedia, Wikidata, and GDELT.

No, the OSINT investigation workflow uses Python stdlib scripts exclusively, requiring no external dependencies to acquire data, resolve entities, and compile cross-source findings.

Building verifiable evidence chains from open data sources like CourtListener and GDELT requires normalizing fetched records into unified entities and linking cross-source references into a structured findings JSON with supporting traceable evidence.

The best way to resolve entities across ICIJ Offshore Leaks and NYC ACRIS records is using scripted Python workflows that fetch, normalize, and cross-link public data to produce verifiable evidence chains for due diligence.