osint-investigation

Cross-reference public records for OSINT by aggregating fragmented data from SEC EDGAR, USAspending, and OFAC sanctions datasets. The skill normalizes heterogeneous entries using tiered confidence levels to uncover corporate accountability leads. It requires Python standard library execution to correlate disparate government registry inputs.

You can identify conflicts of interest by applying statistical timing correlation tests to lobbying filings and federal contract awards. This analysis detects suspicious timing patterns between regulatory actions and contract distributions, linking a company's lobbying activity to its federal awards and offshore corporate ties.

This OSINT investigation process requires only the Python standard library execution environment, with no external dependencies. You can execute the scripts to normalize and correlate data from government and global registries immediately, without installing additional frameworks or packages.

Limitations include reliance on available public records from sources like ICIJ offshore leaks and Senate lobbying databases, which may be fragmented. The statistical timing analysis and tiered confidence matching depend on data quality, potentially missing entities with highly altered names or incomplete registry entries.