The Challenge
One paid account. Six active users. Your subscription revenue is bleeding. Users share passwords with friends, family, and strangers. Premium accounts get passed around like party invitations. Per-seat licenses get split across entire teams. Your infrastructure strains under unexpected load while your revenue stays flat.
You need to know: is this legitimate multi-device usage or unauthorized sharing?
How TrustPath Detects Credential Sharing
When a user accesses your service, our device fingerprinting technology identifies the unique device. We track simultaneous sessions, analyze usage patterns, and detect when accounts are being shared beyond fair use. You receive clear insights: single user with multiple devices, or multiple users sharing credentials.
Real-World Scenarios
Scenario 1: Streaming Service Password Sharing
A premium streaming account shows activity from six different devices across four cities—simultaneously. Mom in Boston, college student in Austin, friend in Seattle, and three unknown devices. TrustPath identifies the distinct device fingerprints, flags the excessive simultaneous usage, and detects impossible geographic patterns. You can now enforce household limits, prompt for account verification, or convert sharers into paying subscribers.
Scenario 2: SaaS Seat License Abuse
Your SaaS platform charges per user seat. You notice one "Premium" account with 12 different devices logging in throughout the day from various locations. TrustPath's device fingerprinting reveals 12 distinct users sharing a single license. You detect the abuse, enforce your per-seat model, and recover lost revenue by prompting the account owner to upgrade to a team plan.
Scenario 3: Educational Platform Account Sharing
A student buys access to your premium course library, then shares credentials with their entire study group. Eight students rotate access to avoid simultaneous logins. TrustPath tracks device fingerprints over time, identifies the pattern of different devices accessing the same account in sequence, and flags coordinated sharing behavior. Course access protected, revenue preserved.
Scenario 4: Gaming Subscription Ring
A group creates a credential-sharing ring for your premium gaming service. They coordinate schedules to avoid simultaneous logins and use VPNs to mask locations. TrustPath's behavioral analysis detects the unusual access patterns—predictable rotation schedules, diverse device fingerprints, and usage behavior inconsistent with a single user. Sharing ring identified and addressed.
What You Achieve
Protect Subscription Revenue Stop revenue leakage from password sharing. Convert shared accounts into individual subscriptions without creating friction for legitimate users.
Enforce Fair Use Policies Implement and enforce household limits, device caps, and usage policies based on accurate detection of sharing behavior.
Accurate User Analytics Stop data pollution from shared accounts. Get true insights into user behavior when each account represents actual individual usage.
No Legitimate User Friction Distinguish between a user switching devices and multiple users sharing credentials. Your real customers experience zero disruption.
What We Check
Device Fingerprinting We create unique identifiers for each device based on hardware characteristics, browser properties, and system configurations. Even when users clear cookies or use incognito mode, we can identify returning devices and track which devices access each account.
Simultaneous Session Detection We identify when the same account is accessed from multiple devices at the same time, especially from different geographic locations. Impossible usage patterns get flagged immediately.
Usage Pattern Analysis We analyze login times, session duration, content preferences, and behavioral patterns to distinguish between legitimate multi-device use (user traveling with laptop and phone) and unauthorized sharing (multiple distinct users with different habits).
Geographic & IP Intelligence We cross-reference device data with IP locations and geographic patterns. A user accessing from their home, office, and mobile device shows legitimate patterns. The same account streaming from New York, London, and Tokyo simultaneously indicates sharing.
