Person / Name: Data Point Intelligence Guide

A name is the weakest identifier in intelligence work and the one most likely to end a career when handled badly. Treat every person record as a hypothesis with a legal basis attached.

Understanding the Person / Name as an intelligence artifact

A person data point is a claimed human identity: given name, family name, aliases, date of birth, nationality and role. Structurally it is a bundle of weak attributes, none of which is unique on its own, that becomes an identifier only through combination. Names vary by transliteration, cultural ordering, diminutives, marriage and deliberate variation, so a person record is best modelled as a cluster of assertions with confidence and provenance rather than as a single authoritative row. It represents the subject of identity resolution, not its result.

Analytically, distinguish a legal identity evidenced by official records from a persona assembled from online activity. Corporate registries, sanctions lists and court records give structured, attributable identity attributes; social platforms give self-asserted ones. The strongest resolution comes from linking attributes that are hard to fabricate at scale, such as a company directorship with a date of birth month and year, rather than from name string similarity.

Why it matters

Identity resolution is what converts scattered technical indicators into accountable actors: a domain registrant, a company director, a sanctioned individual, a beneficial owner. It is also what enables due diligence, insider threat investigation and law enforcement referral. Done rigorously, it produces defensible attribution with documented provenance. Done carelessly, it produces false accusation, and because a person cannot be de-identified once published, the cost of an error here is categorically higher than for any technical artifact.

What analysts actually look for

These are the concrete, observable signals that carry weight in this area of work:

• Officially registered attributes such as directorships, company addresses, appointment dates and partial dates of birth from corporate registries.
• Name variants across transliteration systems, cultural name ordering and diminutives that must be modelled to avoid false negatives.
• Sanctions, PEP and law enforcement list matches with the identifying attributes those lists publish for disambiguation.
• Documented associations through shared company officerships, shared registered addresses or co-signature on filings.
• Professional history and employer claims that can be cross-checked against corporate filings and public announcements.
• Court, insolvency, and regulatory enforcement records that establish adjudicated facts rather than inference.
• Consistency of a claimed identity across independent sources, where a persona existing only on one platform is a weak identity.
• Temporal coherence, where appointment dates, stated education and employment history either fit a plausible life timeline or do not.

Where the data comes from

Authoritative and openly available collection points. Always confirm licensing and terms before operational or commercial use:

• OpenCorporates — Cross-jurisdiction company and officer records linking individuals to directorships, addresses and appointment dates.
• National company registries such as Companies House — Authoritative filings, officer appointments, partial dates of birth and registered addresses in that jurisdiction.
• OpenSanctions — Consolidated sanctions, PEP and watchlist data with structured identifying attributes and source citations.
• OFAC SDN and EU consolidated sanctions lists — Primary source designations with aliases, dates of birth and identification numbers for screening.
• Court and judgment registers published by the relevant jurisdiction — Adjudicated findings, insolvency and disqualification records attributable to a named individual.
• Aleph and OCCRP data collections — Investigative document corpora including leaks and registries, aggregated for cross-source name searching.
• Professional and academic registers where publicly maintained — Verification of claimed qualifications, professional licences and regulated standing where a public register is maintained.

A working method

A repeatable sequence beats ad-hoc searching. This is a practical starting workflow:

1. Fix the purpose and basis — Record why this individual is in scope, the lawful basis for processing their data, and the specific question the research must answer before searching.
2. Establish the seed attributes — Capture the exact spelling, known variants, approximate age, jurisdiction and role, and note which of these are asserted rather than evidenced.
3. Search structured records first — Query corporate registries, sanctions lists and court records, which provide attributable, dated attributes usable for disambiguation.
4. Disambiguate deliberately — Use at least two independent corroborating attributes before merging records, and keep candidate identities separate until that threshold is met.
5. Map documented associations — Build relationships only from evidenced links such as co-directorships or shared filings, not from inferred social proximity.
6. Grade every assertion — Assign source reliability and information credibility to each claim, and carry that grading through to the report rather than flattening it.
7. Review before release — Have a second analyst check the identification chain, and record what was excluded and why, since exculpatory reasoning matters as much as inclusion.

How this connects across the intelligence taxonomy

Intelligence work does not respect neat boundaries. The mission domain you are working, the disciplines you practise, and the data points you pivot on are one connected system. These are the direct relationships for this entry — every link is also a tag, so you can follow any thread across the whole library.

Collected by these disciplines

• Human Intelligence — Information from People, Ethically Obtained
• Criminal Intelligence — Intelligence Supporting Criminal Investigation
• Legal Intelligence — Law, Litigation, and Regulatory Intelligence
• Geospatial Intelligence — Intelligence Derived from Place
• Financial Intelligence — Following Value Through the Financial System
• Social Media Intelligence — Intelligence from Social Platforms and Networks
• Corporate Intelligence — Understanding Companies, Structure, and Control
• Identity Intelligence — Resolving and Verifying Who Someone Is
• Cyber Intelligence — Adversary Activity in Networks and Systems
• Accounting Intelligence — Financial Statements and Accounting Analysis

Investigated in these domains

• Insider Threat
• Organized Crime
• Drug Trafficking
• Human Trafficking
• Child Protection
• Gangs & Street Crime
• Kidnap, Hostage & Extortion
• Art & Antiquities Trafficking
• Forced Labour & Modern Slavery
• Financial Crime

Pivots to these data points

• Email Address — Electronic mail address tied to an individual or organization.
• Username / Handle — Screen name or handle used across online platforms and services.
• Phone Number — Telephone number for voice, SMS, or messaging identification.
• Physical Address — A physical or mailing address tied to a person, company, or registered entity.
• Social Profile — A social media profile or online account page tied to a persona or identity.
• Device / Advertising ID — A mobile advertising or device identifier used in adtech data to track and locate devices.

Inside the platform: where Person / Name lives

The Quantus platform is 204 pages behind a 147-item sidebar organised into six working groups: Command (24 items), Dashboards (15), Threat Theaters (14), Intelligence Domains (15), Investigate (34), and Administration (45). This entry is not a page in isolation — it is a thread running through several of them.

The modules that matter most here:

• search.php — Person / Name profile
• datapoint.php?dp=dp_person — Data point hub
• domain.php?d=insider — Insider Threat dashboard
• domain.php?d=org — Organized Crime dashboard
• domain.php?d=drugs — Drug Trafficking dashboard
• human-trafficking.php — Human Trafficking dashboard
• correlate.php — Correlation graph
• cases.php — Case management

Each dashboard is local-first: it renders from the platform’s own database rather than depending on a live third-party call, so it still works when an upstream API is unreachable or rate-limited. Heavy aggregates are cached with a hard query time cap and degrade to the last good value instead of hanging the page.

Automation, playbooks and AI skills

Analysis that only happens when someone remembers to run it is not a capability. The platform ships a 30-step automation pipeline (cron.php) that collects, ingests, resolves, enriches, correlates and scores on a schedule — 25 seeders, 11 resolvers and 7 enrichment runners, all idempotent and cursor-based so a run can be interrupted and resumed without duplicating or losing work.

AI skills that apply

The 16 one-click operations in ai-skills.php are deterministic jobs, not free-text generation. The ones that matter here:

• Enrichment Runner
• Enrichment → Local
• Correlate Infrastructure
• Summarise (Copilot)
• Generate Report

Alerting closes the loop: rules in alerts.php fire on new indicators matching a saved query, so a first sighting in this area raises a notification rather than waiting to be noticed at the next review.

Feeds, data sources and the API

The collection layer runs a feed registry of free, machine-readable sources — bulk blocklists and trackers (Maltrail, IPsum, FireHOL, the full abuse.ch corpora, phishing databases, Emerging Threats, Spamhaus, DigitalSide, ThreatView), authoritative government feeds (CISA KEV, OFAC, UN and EU sanctions lists), and reference datasets (RIR allocations, ip-to-ASN and geolocation tables, MITRE ATT&CK, EPSS). collect.php pulls them server-side on a schedule; feeds.php and source-catalog.php show what is registered, what it covers and when it last ran.

Anything the platform holds is reachable programmatically. The REST API in api.php exposes 11 endpoints — status, stats, search, lookup, recent, export, bulk_check, top_threats, by_category, categories, check — and export.php streams 18 formats in bounded chunks, so a million-row export neither exhausts memory nor times out:

STIX 2.1, MISP, OpenIOC 1.1, CEF (ArcSight), LEEF 2.0 (QRadar), Zeek/Bro intel, Snort/Suricata rules, Palo Alto EDL, BIND RPZ, hosts blackhole, iptables, CSV, JSON, NDJSON/JSONL, XML.

That covers the CTI standards (STIX 2.1, MISP, OpenIOC), SIEM ingestion (CEF, LEEF, Zeek), detection engines (Snort/Suricata), and direct enforcement (Palo Alto EDL, BIND RPZ, hosts, iptables) — so intelligence developed here can be actioned in the tools you already run, without a manual reformatting step. A TAXII 2.1 server and a MISP/RSS feed are also served for pull-based sharing.

Use cases

Three ways this entry earns its keep in day-to-day work:

1. Triage under time pressure. An artifact or report lands and you need a defensible read in minutes, not days. Fix the purpose and basis is the first move; the platform pre-computes the enrichment so the analyst spends the time on judgement rather than lookups.
2. Building the picture. A single indicator is rarely the story. Search structured records first turns one artifact into a network — shared infrastructure, repeated selectors, the same operator behind different names — via the correlation graph and the cross-entity link engine.
3. Producing something actionable. Analysis that ends in a document nobody can use is wasted. Review before release feeds the case file, the detection rule, the block list or the referral — with sourcing attached so the recipient can verify it.

Case management (cases.php), watchlists, saved searches and scheduled reports mean the work persists between sessions and survives an analyst leaving the team.

How each sector uses Person / Name

The same entry is worked very differently depending on who you are, what authority you hold, and what you are ultimately producing. A military analyst is supporting a commander’s decision; a journalist is meeting a publication standard; an NGO caseworker is protecting a person. The underlying artifacts are shared — the constraints, outputs and thresholds are not.

🎖 Military and defence

Person records in defence work are tightly governed and rarely open ended. Legitimate uses include personnel security and vetting support, force protection where a named individual poses a threat to a facility, counter intelligence, and identification of sanctioned or proscribed individuals in supply chains. Every record requires a documented authority, a defined purpose and a retention period, and collection on protected persons or on nationals of your own state is subject to specific restrictions in most national frameworks. Distinction obligations mean identity assertions carry serious consequences, so person records are reported as graded hypotheses with provenance, never as settled fact, and never assembled from unconstrained open source trawling.

🕵 National intelligence

National intelligence identity work is requirements driven and minimisation bound. A person enters a record because a validated requirement needs them identified, not because they are visible. All source fusion combines registry, sanctions, corporate and lawfully collected material, with strict handling rules for nationals, protected categories and incidentally collected persons. Classification and dissemination controls follow the sensitivity of the collection rather than the sensitivity of the name. Analytical practice is to model identity as a cluster of assertions with source, date and confidence, so that a downstream consumer can see exactly which attributes are evidenced and which are inferred, and can challenge the inference.

👮 Law enforcement

For law enforcement identity resolution must be evidential from the outset. Attributes obtained from official records, registries and lawfully served process carry weight; attributes inferred from name similarity or social media do not. Subscriber identification, financial records and communications data go through production orders, warrants or mutual assistance, and the request package needs the precise identifiers rather than a name. Record provenance for every attribute, because disclosure obligations will require you to show where each came from. A person record supports a charging decision only when identity is proved to the criminal standard by documents, biometrics or admissions, not by aggregation of open sources.

🔍 Private investigation and corporate security

Corporate investigators use person records for pre employment and third party due diligence, sanctions and politically exposed person screening, insider risk and litigation support, all within a lawful basis that is usually contractual necessity or legitimate interests with a balancing test recorded. Private actors may use corporate registries, court records, sanctions lists and published information, and may commission consented background checks. They may not obtain subscriber, financial or location data without process, use pretext to extract personal data from providers, or build covert surveillance profiles. Deliverables should present verified attributes with sources and clearly separate assessment from evidence.

📰 Journalism and OSINT media

Journalistic identification carries the highest public interest justification and the highest personal cost if wrong. Verification standards require at least two independent, documentary sources for any identification, preference for records over inference, and explicit handling of name collision, since common names produce catastrophic misidentification. Source protection may constrain which documents can be cited, so plan the evidential chain accordingly. Right of reply is not optional: put the specific allegations to the named person with adequate time to respond. Do not publish home addresses, family details or unrelated private information, and consider the safety consequences of naming for the person and their relatives.

🌍 NGO, humanitarian and human rights

Human rights documentation is victim centred: survivors and witnesses control what is recorded about them, consent is informed and revocable, and pseudonymisation is the default in anything shared or published. Perpetrator identification for accountability follows the same evidential rigour as legal work, because a wrong name discredits an entire dossier. Do no harm means assessing reprisal risk before recording, sharing or publishing any identity, including for staff and local partners. Berkeley Protocol practice governs preservation and provenance. Store identity records encrypted with strict access control, and plan for what happens to them if the office is raided.

🎓 University and research

Research involving identifiable people requires ethics approval, informed consent where feasible, and a data management plan covering storage, access and destruction. Person level data drawn from public records is still personal data, and institutional review normally applies even where no interaction occurs. Method should describe how identity resolution was performed, how name collision and transliteration were handled, and what error rate the linkage carries, since record linkage error is systematically under reported. Reproducibility is served by publishing linkage code and synthetic or aggregated outputs, with access to identifiable data mediated through a controlled data enclave rather than open publication.

Playbook: working Person / Name end to end

A repeatable sequence, from the moment the requirement lands to the moment a product is delivered and the case is closed out. Each phase states what you are trying to establish, not merely what to click — the point is a defensible chain of reasoning, not a checklist.

Phase 1 — Establish lawful basis and necessity

Before any collection, record the purpose, the lawful basis, why identification is necessary for that purpose, and why less intrusive means are insufficient. Set a retention period and name who may access the record. A good output is a written authorisation covering these points and referencing the case. Stop and escalate if the purpose is curiosity, convenience or a request without a stated basis; there is no acceptable version of proceeding without this.

Phase 2 — Define the identity question precisely

State exactly what must be established: whether a named director is the same person as a sanctioned individual, whether a job applicant's claimed history is genuine, whether a suspect and a registrant are the same. Precise questions bound the collection and prevent profile building. A good output is a single sentence question with the decision it supports. Stop when the question is narrow enough that a specific document could answer it.

Phase 3 — Model the record as assertions

Structure the record as attributes with source, date and confidence rather than as a single row of facts. Names, dates of birth, nationalities and roles are claims made by particular documents at particular times, and treating them as a flat profile is what produces confident errors. A good output is an attribute table where every value is traceable. Stop when no attribute exists in the record without a source and a date attached.

Phase 4 — Normalise names properly

Handle transliteration variants, cultural name ordering, patronymics, diminutives, married and maiden names, and script variations, retaining all forms rather than picking one canonical spelling. Apply matching that accounts for these rather than simple string comparison. A good output is a name variant set with the convention identified. Stop when the variant set is broad enough that a match would not be missed and narrow enough that it does not sweep in unrelated people.

Phase 5 — Prioritise structured official records

Work from corporate registries, sanctions and enforcement lists, court records, insolvency and disqualification registers, gazettes and regulatory filings before touching social sources. These carry attribution, dates and legal weight, and they contain the hard to fabricate attributes such as directorship dates and partial dates of birth that make resolution defensible. A good output is a documentary evidence set. Stop when the structured sources are exhausted before considering inference from unstructured material.

Phase 6 — Test discriminating attributes

Resolve identity on attributes that are hard to coincide: a partial date of birth combined with a directorship, a professional registration number, a company officer identifier, a passport or national identifier disclosed in a filing. Name plus city is not discriminating in any large population. A good output is a match supported by at least two independent discriminating attributes. Stop when the combined probability of coincidence is genuinely low, not merely intuitively low.

Phase 7 — Actively search for the negative

Deliberately look for other people who could match the same attributes, and record them. Name collision is the dominant failure mode in identity work, and a resolution that has not tested alternatives is untested. A good output is a list of excluded candidates with the attribute that excluded each. Stop when you can explain why the match is this person rather than any of the alternatives you found.

Phase 8 — Route sensitive attributes through legal process

Subscriber details, financial records, communications data, travel records and location information come from lawful process to the holder, not from open source inference or commercial aggregators of questionable provenance. Prepare the request with the precise identifiers required. A good output is a served request with a reference. Stop before any attempt to obtain these categories by pretext, purchase from unverified brokers or social engineering.

Phase 9 — Assess special category exposure

Identify whether the record implies political opinion, religion, health, sexuality, trade union membership, criminal allegations or ethnic origin, since these attract stricter conditions in most regimes and are often inferred accidentally through group membership or event attendance. A good output is a note of any special category data with its specific justification. Stop when unjustified special category inferences are removed from the record.

Phase 10 — Grade and document confidence

Assign an explicit confidence to the identification with the evidence supporting it, using a defined scale rather than adjectives. Distinguish confirmed identity, probable identity and candidate identity, and make the distinction visible in every product. A good output is a graded conclusion a reader can challenge. Stop when the grading reflects the weakest link in the chain rather than the strongest piece of evidence.

Phase 11 — Consider consequences before disclosure

Assess what will happen to the person if the identification is wrong, and what will happen if it is right and disclosed. This governs whether to name, when to name, and who to tell. For media and NGO work it also determines right of reply and safety planning. A good output is a recorded disclosure decision with its rationale. Stop when the consequence assessment is complete; identification cannot be undone.

Phase 12 — Retain, restrict and review

Apply access controls limited to the case team, log access, apply the retention period from the authorisation, and schedule review and deletion. Handle subject access, rectification and objection rights according to jurisdiction and any applicable exemption. A good output is a controlled record with a deletion date and an audit trail. Stop when retention is scheduled and the record cannot be reached by anyone without a case need.

The platform ships this as a step-checked workflow in playbooks.php, so progress is recorded against a case rather than held in someone’s head.

Source register: what to collect from, and how

Sources are listed with their access model so you can plan around cost and licensing before you build a dependency on them. Open means no account required; registration means a free account or API key; licensed means paid or institutional access. Always confirm current terms — licensing changes, and a source that was free for research may not be free for commercial or evidential use.

Source	Access	What it gives you	How it is used here
OpenCorporates	Registration	Aggregated company registry data across many jurisdictions covering entities, officers, addresses and filing history with links to source registers.	Establishes directorships and officer records that provide dated, discriminating attributes for identity resolution.
Companies House and equivalent national registers	Open	Official corporate registers holding incorporation records, officer appointments, partial dates of birth, addresses and filing histories.	Primary documentary source for linking a named person to entities with dated, legally filed attributes.
OFAC sanctions programmes and consolidated lists	Open	United States sanctions designations with names, aliases, dates of birth, nationalities, identifiers and programme references.	Authoritative screening source and a rich set of alias and identifier variants for a designated individual.
EU consolidated sanctions list	Open	European Union restrictive measures listings with identifying particulars, legal basis and designation dates for individuals and entities.	Screening and identity attribute source for EU designations, with published reasons supporting the designation.
UN Security Council consolidated list	Open	United Nations designations under sanctions regimes with identifying information, aliases and narrative summaries of reasons.	Global baseline screening list and source of alias variants and identifier data for designated persons.
UK sanctions list	Open	United Kingdom designations with identifying particulars, statements of reasons and regime references published by the government.	Screening for UK designations and corroboration of identifying attributes across sanctions regimes.
Global Legal Entity Identifier Foundation	Open	Open reference data linking legal entity identifiers to entity names, addresses, registration authorities and parent relationships.	Disambiguates entities associated with a person and resolves corporate group structure around directorships.
ICIJ Offshore Leaks database	Open	Searchable database derived from leaked offshore records covering entities, officers, intermediaries and addresses in secrecy jurisdictions.	Identifies offshore structures associated with a person, used as a lead requiring corroboration from official records.
OpenSanctions	Open	Consolidated open dataset of sanctions designations, politically exposed persons and related entities with normalised names and identifiers.	Single consolidated screening pass across many regimes with normalised aliases and structured identifier fields.
Court records and judicial publication services	Licensed	Published judgments, filings, insolvency records and disqualification registers naming parties with dates and case references.	Documentary evidence of legal proceedings involving a named person, with citable references and dates.
Professional and regulatory registers	Open	Statutory registers of licensed practitioners such as doctors, lawyers, accountants and financial advisers with registration numbers and status.	Provides a unique professional identifier that resolves identity far more reliably than any name match.
Land registry and property records	Licensed	Official records of property ownership, charges and transactions tied to named proprietors in a given jurisdiction.	Documentary link between a person and assets, used for asset tracing and corroboration under a lawful basis.
Official gazettes and public notices	Open	Government published notices covering appointments, insolvency, name changes, licensing and other legally required publications.	Dated official record of status changes, including formal name changes that explain apparent identity discontinuities.
Berkeley Protocol on Digital Open Source Investigations	Open	Methodological standard for collection, verification, preservation and analysis of open source material for accountability proceedings.	Framework for documenting identity findings so they survive legal scrutiny and adversarial challenge.
Financial Action Task Force recommendations and guidance	Open	International standards on customer due diligence, beneficial ownership and politically exposed person screening obligations.	Defines the expected standard of identity verification and beneficial ownership analysis in regulated contexts.
Global Investigative Journalism Network resources	Open	Methodological guides and source directories for verification, corporate research and cross border investigation.	Practical methodology and jurisdiction specific registry guidance for documentary identity research.

Prefer sources that publish a methodology and a revision history. A dataset that changes silently is a liability in any product that has to survive challenge.

Tooling

Tools commonly used against Person / Name. None of these replace judgement, and each carries its own failure modes — know what a tool infers versus what it observes.

• Aleph by OCCRP — Search and cross reference person and entity names across leaked and public document collections. Limitation: coverage is uneven and matches require manual verification against primary sources.
• OpenSanctions matching interface — Screens names against consolidated sanctions and politically exposed person data with fuzzy matching. Limitation: fuzzy matching produces false positives that require documentary resolution.
• Record linkage libraries — Probabilistic matching across datasets with configurable comparison and blocking strategies. Limitation: error rates depend entirely on parameter choices that are rarely reported.
• Name transliteration and matching toolkits — Handle script conversion and cultural name ordering during comparison across languages. Limitation: no toolkit handles all conventions, so manual review of edge cases remains necessary.
• Maltego with registry transforms — Graphs relationships between people, companies, addresses and filings for network analysis. Limitation: graph density invites over interpretation of weak links such as shared addresses.
• Case management platforms with attribute provenance — Store identity assertions with source, date, confidence and access controls. Limitation: only as good as the discipline of the analysts populating the fields.
• Document management with hashing — Preserves source documents with integrity verification and retrieval timestamps. Limitation: requires disciplined capture at the moment of retrieval, since records are amended and withdrawn.
• Corporate registry bulk data feeds — Enable systematic search across officers and entities rather than one lookup at a time. Limitation: licensing terms frequently restrict redistribution and bulk reuse.

AI skills and automation in detail

These are deterministic jobs with defined inputs and outputs, not open-ended prompting. Each is idempotent and cursor-based: interrupt one and it resumes where it stopped rather than duplicating work or losing progress.

• Enrichment Runner — Walks the indicator set through a chosen provider in time-boxed, cursor-based batches that resume rather than restart.
• Enrichment → Local — Materialises enrichment into the local store so dashboards render from your own database instead of a live third-party call.
• Correlate Infrastructure — Builds the cross-entity link graph: shared hosting, reused certificates, overlapping registrants, repeated selectors.
• Summarise (Copilot) — Produces a narrative summary beside the underlying records. It explains; it never creates indicators or assigns attribution.
• Generate Report — Assembles a sourced product from the current case or query, with provenance attached to each element.

A note on the boundary: the only skill that involves a language model is Summarise (Copilot), and it writes prose about records that already exist. Nothing else on this list involves generation of any kind. No indicator, relationship or attribution in the platform originates from a model. See the full skill list.

Tradecraft notes

The distinctions that separate a competent analyst from a fast one:

• Name collision is the default assumption, not the exception. In any population of millions, name plus city plus approximate age will match multiple unrelated people, so a resolution that has not enumerated and excluded alternatives is not a resolution.
• Prefer attributes that are expensive to coincide. A directorship with a month and year of birth, a professional registration number or a company officer identifier resolves identity; a matching name and a shared employer does not.
• Record what each document asserts, not what you conclude from it. A filing states that a person of a given name and birth month was appointed on a date, and keeping that granularity is what allows a later analyst to re examine your inference.
• Transliteration is not noise, it is evidence. The specific romanisation used in a filing often indicates the issuing jurisdiction or the system that produced the record, which itself narrows the population.
• Beware the aggregated profile that no single source supports. Combining weak sources produces confidence without evidence, and the resulting record looks far stronger than any of its components justify.
• Formal name changes explain many apparent identity breaks. Gazettes, marriage records and deed poll notices are the documentary bridge, and looking for them is faster than assuming deliberate concealment.
• Sanctions list entries are an underused source of alias variants. Designation records frequently carry a dozen transliterations, dates of birth and identifier numbers that no other public source collects together.
• Write the consequence of error into the case record before you report. Identifying the wrong person cannot be retracted, and forcing yourself to state the harm changes how much corroboration you demand.

Measuring whether it is working

Capability claims should be falsifiable. These are the measures that show whether work on Person / Name is producing anything, and they are worth baselining before you change process or tooling.

• Proportion of identity records where every attribute carries a source and a date, sampled from closed cases rather than self reported.
• Rate of identifications supported by at least two independent discriminating attributes rather than by name matching alone.
• Number of candidate matches formally excluded per resolution, measuring whether alternatives are being tested rather than ignored.
• Frequency of downstream correction or retraction of identity findings, tracked as the primary quality outcome measure.
• Share of person records with a documented lawful basis, purpose and retention date at the point of creation, audited periodically.
• Time from retention expiry to actual deletion, measuring whether minimisation is operational rather than declared.
• Proportion of sensitive attributes obtained through lawful process rather than through inference or third party aggregation.

Beware of measuring volume alone. Indicator counts and report counts rise easily and say little; time-to-attribution, proportion of findings that survive review, and how often a product changed a decision say a great deal.

Common pitfalls

• Common names produce catastrophic false positives, and merging two people into one record can destroy an innocent person's reputation or livelihood.
• Transliteration differences mean the same individual appears under several spellings while different individuals converge on one.
• Data brokers recycle unverified aggregations, so multiple sources agreeing often reflects one original error copied repeatedly.
• Family members frequently share names, addresses and companies, making relatives the single most common source of misattribution.
• Stale records persist indefinitely, so directorships, addresses and affiliations may describe a situation that ended years ago.
• Confirmation bias in identity work is severe, because once a name is proposed every ambiguous fact starts to look confirmatory.

Legal and ethical considerations

Person records are personal data in every major privacy regime and frequently include special category data such as political exposure or criminal allegations, which attract stricter conditions. Document lawful basis, necessity and proportionality before collection, limit the data to what the investigative question requires, restrict access, and set a retention period with review. Subjects may hold rights of access, rectification and objection depending on jurisdiction and exemption. Never publish or share unverified identification, and keep an audit trail linking every assertion to its source and date.

Data integrity: no fabrication, no drift, no hallucination

Intelligence that cannot be traced back to a source is not intelligence, it is assertion. Everything in this entry — and everything in the platform behind it — is built on a small number of non-negotiable rules.

Provenance on every record

Every indicator carries the source that supplied it, a first-seen and last-seen timestamp, and a sighting count. Where several feeds report the same artifact, each contribution is recorded separately rather than collapsed, so you can see whether a finding rests on one source or twelve. Source attribution travels with the data into every export, so a recipient can audit a claim without asking you for the working.

Nothing is invented to fill a gap

If the platform has no data for Person / Name, it says so. Empty is displayed as empty — never padded with plausible-looking placeholder values, sample records or illustrative examples that a reader might mistake for observations. A dashboard with no rows is a true statement about collection coverage, and it is treated as a gap to close, not a blemish to hide.

Scoring is deterministic and reproducible

Threat scores, reputation grades and risk tiers are computed from stated inputs with fixed weights, not estimated. The same inputs always produce the same output, and the formula is visible rather than a black box. Aggregates are cached with an explicit time-to-live so a figure on screen is never silently stale — and when a heavy query exceeds its time budget the platform serves the last known-good value and labels it, rather than inventing a fresh number or hanging.

Where AI is used, and where it is not

Language models summarise and explain. They do not create indicators, assign attribution or manufacture relationships. No IP address, wallet, hash or identity in the platform originates from a model — every one is ingested from a named feed, resolved from a reference dataset, or entered by an analyst with a source recorded. Copilot output is presented as narrative alongside the underlying records, never in place of them, so a reader can always check the summary against the evidence.

Guarding against drift

Enrichment is additive and timestamped rather than overwriting. Reference data — sanctions lists, allocations, taxonomies — is re-synchronised from the authority on a schedule instead of being edited in place, so local copies cannot quietly diverge from the source of truth. Attribution is recorded with a confidence level and the reporting it rests on, and inferred relationships are labelled as inferred. When a source retracts or corrects, the correction propagates rather than leaving a stale assertion behind.

What this means for you

You can put a finding from this platform in front of a regulator, a court, a board or a partner agency and show where each element came from. That is the standard the tooling is built to — because in this work, being confidently wrong is more damaging than being usefully uncertain.

By the numbers

The taxonomy this entry belongs to is not a marketing list — it is the actual structure of the platform: 52 mission domains, 52 intelligence disciplines and 65 data points, each with a live dashboard behind it. Supporting that: 18 indicator types, 14 playbooks, 16 AI skills, 18 export formats and a 30-step automated pipeline.

This particular entry connects directly to 10 intelligence disciplines, 10 mission domains, 6 closely related entries — every one of them a tag you can follow, and a dashboard you can open.

Questions analysts actually ask

How much evidence do I need before naming someone?

Enough that you can explain why it is this person and not any of the alternatives you actively looked for. In practice that means at least two independent discriminating attributes from documentary sources, an explicit search for name collisions with the excluded candidates recorded, and a confidence grade that reflects the weakest link rather than the strongest evidence. For publication or a charging decision the bar is higher still and includes right of reply or the criminal standard respectively. If you cannot articulate the exclusion argument, you have a candidate, not an identification.

Can I use a commercial people search or data broker service?

Approach with caution and check provenance before anything else. Many aggregators combine records of unknown origin, including scraped and breached material, and using data that was unlawfully obtained can taint your work and expose your organisation. Where a service is used, establish where the underlying data came from, whether the subjects had any lawful basis applied, and whether your jurisdiction restricts the sale of such data. Prefer official registers, which are attributable, dated and defensible. Never treat aggregator output as verification; treat it as an unverified lead.

What is the safest way to handle a partial date of birth?

Treat it as one of your most valuable attributes and one of your most sensitive. Many corporate registries publish month and year while withholding the day precisely because the combination is discriminating without being sufficient for impersonation. Use it for matching, record it with its source, and do not publish it. When combined with a directorship date or a professional registration it dramatically reduces collision probability. Where a full date is available from a leaked or unofficial source, weigh whether processing it is lawful and necessary before it enters the record at all.

How should I handle a subject access request on an investigation record?

Take it seriously and route it through your data protection function immediately rather than answering ad hoc. Most regimes provide exemptions for the prevention and detection of crime, for regulatory functions and sometimes for journalism, but exemptions are narrow, must be justified case by case, and do not permit blanket refusal. This is one of the reasons every attribute needs a source and every record needs a documented purpose: without them you cannot demonstrate the exemption applies. Log the request, the assessment and the outcome.

Is aggregating public information about a person lawful?

Not automatically. Regulators and courts have repeatedly held that combining individually public items into a profile creates a new and more intrusive processing activity requiring its own justification. The relevant test is purpose and proportionality: a targeted check against a specific question is defensible, whereas assembling a comprehensive life profile because the material was accessible is not. Collect only what the stated question requires, avoid inferring special category attributes, and record the necessity assessment. The availability of data is never the basis for processing it.

What do I do about aliases and transliteration variants?

Collect them systematically rather than picking one spelling. Build a variant set covering script conversions, common romanisation systems, name ordering conventions, patronymics, diminutives and known aliases, then search across the whole set. Sanctions designations are an excellent source of variants because they aggregate many spellings for one individual. Retain every variant with the source that used it, since the particular spelling in a document is itself evidence about which system produced it and which jurisdiction the record came from.

How do I avoid special category data creeping into a record?

Watch inference, not just collection. Membership of an organisation can reveal political opinion or religion, event attendance can reveal both, health can be inferred from charity involvement, and criminal allegation data attracts specific conditions in most regimes. Review the record deliberately for attributes that imply these categories, remove anything not strictly necessary for the stated purpose, and where retention is necessary, record the specific condition relied upon. This review should happen before dissemination, because once a product circulates the inference travels with it.

Standards, frameworks and further reading

Work that references a recognised framework is easier to defend, easier to hand over, and easier for a partner to consume:

• GDPR Articles 5, 6, 9 and 14 with equivalent national law, governing lawful basis, minimisation, special category conditions and transparency for person data.
• Law Enforcement Directive and equivalent police data regimes, setting distinct rules for processing personal data for criminal law purposes.
• FATF Recommendations 10, 12 and 24, defining customer due diligence, politically exposed person handling and beneficial ownership expectations.
• Berkeley Protocol on Digital Open Source Investigations, setting verification, provenance and preservation standards for identity findings used in accountability work.
• ISO/IEC 27701 privacy information management, providing controls for processing personally identifiable information within a security management system.
• National sanctions regimes and their designation criteria, which define the identifying particulars published and the screening obligations that follow.
• Editors code of practice and equivalent journalistic standards, governing accuracy, privacy, right of reply and treatment of relatives in identification.
• Institutional research ethics frameworks and human subjects review requirements, which govern academic processing of identifiable person data.

References

Primary sources and authoritative references for this entry. Publishers revise and retire material, so treat the retrieval date as part of the citation and re-check before relying on any of it in a formal product.

1. OpenCorporates — OpenCorporates. Aggregated open company registry data covering entities and officers across many jurisdictions.
2. OFAC sanctions programmes and information — US Department of the Treasury. Authoritative United States sanctions designations with identifying particulars and aliases.
3. United Nations Security Council Consolidated List — United Nations. Global sanctions designations with identifying information and narrative summaries.
4. OpenSanctions — OpenSanctions. Consolidated open dataset of sanctions and politically exposed person records with normalised identifiers.
5. Global Legal Entity Identifier Foundation — GLEIF. Open reference data linking legal entity identifiers to entity and ownership information.
6. Offshore Leaks Database — ICIJ. Searchable database of entities, officers and intermediaries derived from offshore records leaks.
7. FATF Recommendations — Financial Action Task Force. International standards on due diligence, beneficial ownership and politically exposed persons.
8. Berkeley Protocol on Digital Open Source Investigations — UN OHCHR. Standard for verification, provenance and preservation of open source material in accountability work.
9. Guide to investigating companies and people — GIJN. Methodological resources for documentary research on individuals and corporate structures.
10. The Gazette official public record — The Gazette. United Kingdom official notices including insolvency, appointments and formal name changes.

Link integrity: every reference above was verified with a live request when this page was generated. Where a publisher had moved or withdrawn a document, the link was repointed at a preserved copy in the Internet Archive and marked as archived. Anything with no reachable copy anywhere had its link removed rather than left to rot — the source is still credited, it simply cannot be linked.

Put it into practice

The Quantus Intel threat intelligence platform operationalises this entry: resolves identities from structured registry and sanctions data with provenance, confidence grading and access-controlled case records. Explore the platform, or browse the rest of the library by following any tag above.