Digital Executive Protection (DEP) with DigitalStakeout

The Problem

Executive protection has a blind spot. Physical security teams know how to manage travel routes, secure residences, and vet event venues. But the digital surface area of a protectee — the sprawling, constantly expanding footprint of personal information, corporate associations, family connections, and historical records scattered across the open, deep, and dark web — is largely unmonitored. A threat actor doesn't need to breach a firewall to build a targeting package on an executive. They just need Google, a few data broker sites, and some patience.

The challenge isn't access to data — it's that there's too much of it, most of it is noise, and the signal only becomes visible when you have enough context about the person you're protecting to know what matters.

What DEP Is

Digital Executive Protection is the continuous monitoring, enrichment, and AI-driven risk evaluation of a protectee's digital exposure across the surface web, social media, dark web, breach databases, data brokers, public records, and domain registries.

The goal is two fold: map the protectee's complete digital footprint so you know what an adversary can find, and detect emerging threats in real time so you can act before exposure becomes exploitation.

How the DSO Feed Powers DEP

DigitalStakeout's feed system is the engine behind DEP. It operates as a four-stage pipeline: seed, expand, enrich, and evaluate.

Stage 1: Seed the Identity

An analyst creates a feed for a protectee and populates it with whatever identity selectors are known. This might be minimal — a legal name and a corporate email — or it might be extensive. The DSO feed interface supports over 20 categories of identity selectors:

• Screen Names — social media handles, forum aliases

• Handles — platform-specific identifiers on messaging apps (Signal, WhatsApp)

• Usernames — credentials on SaaS platforms, financial apps, online communities

• Legal Name — official name as it appears in public records

• Email Addresses — personal, professional, and disposable accounts

• Phone Numbers — mobile, landline, VoIP

• Physical Addresses — home, office, frequently visited locations

• Device IDs — IMEI numbers, advertising IDs

• Network IDs — MAC addresses, IP-based identifiers

• Government IDs — driver's license, passport numbers (where lawfully permissible)

• Vehicle IDs — license plates, VINs

• Monikers — nicknames, pseudonyms, alternative names

• Crypto IDs — wallet addresses, blockchain identifiers

• Corporate Codes — employee IDs, internal reference numbers

• Alternative References — other ways the individual is referred to in records

• Keywords — associated terms, phrases, or topics

• Places — geographic locations with polygon-based geofencing

• URLs — personal blogs, professional profiles, owned web properties

  
  

Each selector category is purpose-built. You don't search for a phone number the same way you search for a screen name — each data type maps to specific collection sources where it's most likely to surface.

Stage 2: Fan Out Across Sources

Every selector gets applied against the collection sources relevant to its data type. The system calls these "sets" — and the mapping is deliberate:

Within each source, sub-sources can be tuned: surface web, Bluesky, news, Twitter/X, social platforms, dark web, archive, and risk event feeds. An analyst protecting a CEO might enable all sub-sources for chatter monitoring but restrict a CFO's feed to news and financial sources only.

Positive and negative term filters and domain exclusions act as pre-filters, reducing noise before content ever reaches the AI evaluation layer.

Stage 3: Enrich and Build Ground Truth

This is the critical stage and the one most people underestimate. The initial selectors are just seeds. The real value comes from what the system discovers.

A name search surfaces email addresses from corporate directories. An email search hits breach databases and reveals associated passwords, IP addresses, and linked accounts. A screen name search uncovers connected profiles on other platforms. A phone number shows up on a data broker site alongside a home address and family members' names. Each discovered attribute becomes a new variable in the protectee's identity graph — and potentially a new selector that feeds back into collection.

The objective is to assemble as much "ground truth" as possible — verified, correlated facts about the protectee's digital presence. Without this enrichment step, the AI evaluation layer has nothing meaningful to work with. You can't assess risk to someone whose digital surface you haven't mapped.

Consider what a single Instagram post can reveal: an old photo comment exposes a protectee's sister's name, her married name, the names of her grandchildren, and the city they live in. That's not just a privacy exposure — it's a social engineering attack surface. But the AI can only flag it as such if it knows who the protectee's family members are. Enrichment is what closes that gap.

Stage 4: Per-Entity AI Risk Evaluation

Once the feed has collected and enriched across all selector categories and sources, the accumulated intelligence gets condensed into a structured context package for the protectee. This is what gets passed to the LLM for threat analysis.

This must be done per entity. If you batch multiple protectees into a single evaluation pass, the context collapses. The AI can't distinguish which threat indicators belong to which person, which associations are meaningful, and which risk factors apply to whom. Identity context is what makes the evaluation work, and identity is inherently individual.

The AI evaluates each piece of incoming content against the protectee's full enriched profile and produces a structured annotation — the machine-readable output that drives every downstream workflow.

The Annotation Layer: What the AI Produces

Every event that flows through the feed gets stamped with a structured annotation. These fields are the foundation for rules, analytics, alerting, routing, and reporting.

Category: DEP

The classification tag identifying the event as a Digital Executive Protection finding. This is the primary category for events processed by DigitalStakeout for this task.

ReferenceID: dep-ai-v4.2

The AI model version that produced the assessment. This is critical for three reasons: auditability (every decision traces back to the exact evaluation logic that made it), model improvement (you can compare v4.2 decisions against v4.3 to measure accuracy gains), and accountability (if a decision was wrong, you know which version to investigate).

Decision

A natural language assessment generated by the AI explaining why this event matters in context. This isn't keyword matching — it's contextual reasoning. Examples:

• "Exposed family members' names and relationship details"

• "Exposed corporate phone number and location"

• "Potential association with influential individuals and organizations could expose sensitive insights"

• "Exposed full name, role, and corporate positions"

• "Contains specific date that could imply scheduled event info"

• "Benign content, no threats or risks detected"

The decision text is what separates AI-driven DEP from simple alert-on-keyword monitoring. The model evaluates what the exposed information means for the specific protectee, not just whether a selector matched.

Indicators

The specific extracted data points found in the content:

• phone:123-456-7890

• legal_name: john jay smith

• name: john smith

• url: example[.]com

• company_name: acme inc.

These are enrichment artifacts. Every indicator discovered is a potential new selector that feeds back into the identity graph, expanding ground truth.

Mentions

How the content matched the protectee's selectors:

• indicator_match — Direct hit on a known, tracked selector. Highest confidence.

• indicator_partial — Associated or partial match. Requires context to assess relevance.

• indicator_no — Matched by name or context but not a specific tracked indicator. May be a false positive.

This distinction is essential for precision. A direct indicator match on a known phone number is actionable. An indicator_no hit on a common name fragment appearing in an unrelated article about juniper trees or a hairstylist's Instagram is noise — and the system needs to distinguish between them.

Risk Level

The final severity classification: Benign or High Risk. This is what drives workflow routing.

• A professional bio on the company's investor relations page → Benign. Logged, indexed, no alert.

• An Instagram post exposing six family members by name with relationship details → High Risk. Immediate escalation.

• A data broker site listing phone numbers and a full legal name → High Risk. Removal action initiated.

• A news article mentioning the protectee in a routine earnings report → Benign. Context captured, no action needed.

  
  

Source Reputation

Domain ranking of the source: Top_1K_Domain, Top_10K_Domain, Top100K_Domain, Top10M_Domain, Top25M_Domain, Unranked_Domain.

This feeds credibility and reach weighting. An exposure on nytimes.com or instagram.com (Top 1K) has vastly greater discoverability than something buried on an unranked domain. Reputation scoring helps prioritize which exposures demand immediate attention versus which get queued for routine review.

From Annotations to Action

Every annotation field becomes a rule predicate. The structured output enables workflows like:

• Immediate escalation: category = DEP AND risk = High Risk AND mentions = indicator_match AND reputation = Top_1K_Domain → Alert executive protection team

• Family exposure routing: decision contains "family" AND risk = High Risk → Route to family security coordinator

• Data broker remediation: decision contains "data broker" OR indicators contain phone/address AND source = intelius/spokeo/whitepages → Initiate removal request workflow

• False positive suppression: mentions = indicator_no AND risk = Benign → Log only, no notification

• Enrichment feedback: New indicators discovered → Feed back into configuration, expand collection

The annotation layer is what makes DEP operationally scalable. Without it, you have a firehose of raw web mentions. With it, you have a prioritized, contextualized, routable intelligence feed.

Why Per-Entity Context Is Non-Negotiable

The entire pipeline depends on maintaining isolated, enriched context for each protectee. Here's why:

Without enrichment, the AI sees a name match and can't distinguish between a CEO and a hairstylist who shares the same name. It can't recognize that a phone number on a data broker site belongs to the protectee's associate. It can't identify family members mentioned in an Instagram comment. Every event gets the same shallow evaluation, and the signal-to-noise ratio makes the output useless.

Without per-entity isolation, the AI conflates indicators across protectees. If you're monitoring a CEO and a CFO from the same company in a single context window, the model can't determine whether a breach exposure belongs to one or the other, whether a social media mention is relevant to the CEO's personal risk or the CFO's, or whether a discovered family member name is associated with the right protectee. The analysis fails.

The architecture is deliberate: one feed per protectee, continuous enrichment expanding ground truth, per-entity context assembly, and individual AI evaluation. That's how you get from "we have a name and an email" to "we know every discoverable fact about this person's digital exposure and we're evaluating threats against that full picture in real time."

The DEP Operating Model

This is a closed loop. The more the system discovers, the better it gets at discovering more. The more context the AI has, the more precise its risk assessments become. And every assessment is versioned, auditable, and routable — turning raw digital noise into actionable executive protection intelligence.