Data Points

👤 Person / Name

🔧 Native workbench

🔗 Analyst pivots

🔍 Lookup

📜 Playbook — Person / Name exploitation

1. Direction — frame the requirement for Person / Name: what decision does this support, by when?
2. Collection — pull the 0 mapped sources (0 free) and the native workbench (open); capture provenance and observe OPSEC.
3. Processing — normalize, de-duplicate and enrich the collected data.
4. Analysis — correlate against local holdings; apply ACH; assign confidence.
5. Dissemination — open a case, draft a report, share via STIX/MISP.
6. Feedback — set an alert rule / watchlist to monitor for change.

⚡ AI Skills & Automation

Automate unattended via the cron pipeline (collect → ingest → resolve → enrich → score → alert).

🗃 Data Management

🧩 Advanced Capabilities

✨ Enrichment pathways

🎫 Person / Name

A person data point represents a natural individual identified by name and associated biographical attributes (aliases, DOB, nationality, roles, affiliations). It anchors identity-centric investigations, letting analysts consolidate scattered selectors (emails, phones, accounts) into a single resolved human entity for attribution, due diligence, and sanctions screening.

Format: Free-text full name with optional structured attributes (given/family name, DOB YYYY-MM-DD, place of birth, nationality ISO-3166). No canonical validation; requires transliteration/romanization handling and alias normalization.

📡 How it is collected

• Public records and corporate registries (directors, UBOs)
• Leaked/breach datasets correlating names to selectors
• Social media profiles and about pages
• News, court filings, and litigation records
• Sanctions, PEP, and watchlist entries
• HUMINT / interview-derived attribution

📚 Enrichment & validation sources

🔗 Pivot to

🧩 Analysis & hunting techniques

• Name disambiguation and entity resolution across sources
• Alias/transliteration expansion (soundex, phonetic matching)
• PEP and adverse-media screening
• Corporate network / UBO graph traversal
• Selector-to-identity clustering
• Timeline reconstruction of roles and affiliations
• Fuzzy matching against sanctions lists with scoring
• Cross-jurisdiction record correlation

🔧 Tools

• Maltego
• OpenRefine
• SpiderFoot
• Aleph
• OCCRP Data
• Dedupe.io
• Neo4j
• Pipl-style resolution frameworks

⚡ Workbench actions

• Screen against sanctions and PEP lists
• Run adverse-media sweep
• Resolve aliases and transliterations
• Map corporate/UBO network
• Link known selectors to the identity
• Build affiliation timeline
• Score identity-match confidence
• Export entity graph to link analysis

📊 Dashboard KPIs

📂 Open