How This Briefing Works
This report opens with key findings, then maps the gaps between what Enrichley discloses and what BLACKOUT observed at runtime. From there: what it means for your organization, what to do about it, and the detection data and evidence underneath.
Key Findings
Pre-Consent Activity
Enrichley was observed loading and executing before user consent was obtained on 100% of sites where it was detected.
Claims vs. Observed Behavior
pending
“Unknown”
Requires claims extraction via CDT
What This Means For You
What To Do About It
Role-specific actions based on observed behavior
If You Use Enrichley
- →Demand contractual transparency on identification methodology and match rate accuracy
- →Require data processing agreement explicitly prohibiting visitor data sharing with other customers
- →Implement consent-first deployment where behavioral capture only activates after explicit opt-in
- →Configure CRM integration to log all enrichment data sources for GDPR Article 15 compliance
If You're Evaluating Enrichley
- →Request third-party audit of consent bypass mechanisms and post-rejection tracking
- →Evaluate alternative visitor identification tools with documented GDPR compliance
- →Consider first-party identification strategies (gated content, progressive profiling) that don't rely on third-party data networks
- →Assess total cost of ownership including legal review, compliance monitoring, and potential regulatory defense
Negotiation Leverage
- →Enrichley VRS 80 = Broker (90) + Counselor (100) threat. Demand transparency on data sharing practices and consent mechanisms before renewal.
- →Behavioral biometrics (BTI-C06) + session recording (BTI-C07) = special category data under GDPR. Require explicit legal basis documentation and DPA amendment.
- →Consent bypass (BTI-C09) detected in runtime observation. Request technical remediation plan with third-party verification or consider contract termination.
- →Match rate accuracy directly impacts attribution model integrity. Demand SLA on false positive rates and methodology transparency.
- →Visitor data sharing across customer base = competitive intelligence leakage. Negotiate exclusive data processing or seek alternative vendors.
- →Ask: What is the documented legal basis for behavioral biometrics collection? How are individuals notified? What is the opt-out mechanism? Expect vague answers.
Runtime Detections
BLACKOUT observed this vendor's JavaScript executing in a live browser and classified each hostile behavior using our BTI-C (Behavioral Threat Intelligence — Capability) taxonomy. These are not theoretical risks — each code below was triggered by something we watched this vendor's code actually do.
Evasion infrastructure, auditor bypass
Impact: Tag-level deception allows Enrichley to present different behavior to privacy tools versus actual data collection, defeating user consent mechanisms and creating regulatory liability.
Keystroke/mouse tracking
Impact: Continuous capture of mouse movements, keystroke dynamics, and scroll patterns creates unique user signatures that enable cross-device tracking and persistent identification even after cookie deletion.
Full session replay
Impact: Full session capture including form inputs, page interactions, and navigation paths creates PII exposure risk and enables behavioral profiling that users cannot detect or control.
Identity stitching
Ignoring CMP signals
Impact: Tracking continues after rejection, violating the foundational principle of user choice and creating per-violation GDPR fines of €20M or 4% global revenue.
Device identification
Impact: Browser and device fingerprinting creates persistent identifiers that survive cookie deletion, defeating user privacy controls and creating long-term tracking liability.
PII deanonymization
IOC Manifest
Indicators of compromise across 4 categories. Use for detection rules, CSP policies, or Pi-hole blocklists.
Ecosystem & Supply Chain
Evidence Artifacts
Artifacts collected during analysis, available with evidence-tier access.
Complete network capture with all requests and responses
157 detection signatures across scripts, domains, cookies, and network endpoints