OSINT DATA AGGREGATION PIPELINE

Intake & de-duplication. Multiple feeds (APIs, scrapes, hand-curated tips) collapse into a queue with content hashes, fuzzy URL canonicalization, and near-duplicate detection. The system saves time by not asking humans to re-read the same story with a different UTM tag.

Normalization & schema. Everything lands in a slim common schema—entities, events, places, times, and links—so cross-source joins don’t become regex archaeology. Where fields don’t map, we keep a raw sidecar for full-fidelity retrieval.

Provenance & chain of custody. Each transformation appends to a provenance trail (source URL, access date, transform version, human edits). Exports include this trail so downstream readers can audit without phoning the original collector.

PII safety. Default to minimization: redact or hash sensitive fields, segregate storage, and require higher privileges for re-identification. Automated checks flag accidental PII (faces, license plates) and route for human review before publication.

Source scoring. Reliability scores are evidence-based: outlet track record, author identity confidence, corroboration count, and historical correction rate. Scores decay over time and update when retractions land.

Alerts & thresholds. Instead of “ping for everything,” alerts require a rule that combines source score + topic + location + novelty. Analysts can subscribe to saved queries and receive a digest with diffs, not a firehose.

Reproducible exports. Every chart/table in the reporting layer can be regenerated from a saved query with pinned transform versions. If a result made it into a brief, there’s a button to see the lineage, no hand-waving.