Protecting the online gaming industry from increasingly complex attack patterns
Arkose Labs goes beyond traditional fraud detection to address abuse of virtual economies, bot-driven in-game cheating and bonus abuse. Going further than mitigation-focused strategies, it provides powerful remediation against bot and human-driven attacks without disrupting good users.
Real-time analysis, combined with targeted friction, causes automated scripts to fail, and saps the time and resources required to circumvent defenses at scale.



“The difference between Arkose Labs and our past solution is night and day for us. Previous defenses created a bad user experience, while Arkose Enforce solves our problem and makes it fun for our users”
Antoni Choudhuri, Engineering Director
Roblox
Online gaming fraud prevention solution
In-Game
Bot Abuse
Eliminate bogus gaming sessions, cheating services, win-loss trading and account level-ups from automated scripts.
ATO and
Reselling
Protect against fraudsters looking to hack into genuine accounts or carry out account reselling to offer cheap access to premium games.
Fake New Accounts
Detect bots and human sweatshops creating new malicious accounts that are used to ‘game’ the platform and players. Detect malicious users from the outset and prevent downstream banning.
Real Money Trading
Protect against auction house abuse and bot-driven farming of in-game assets including gold and currencies, to sell on through unofficial marketplaces.
Spam and Scams
Protect all live chat and other communication channels from malicious content and scams targeting good users.
Bonus Abuse
Launch promotions and new customer acquisition giveaways, with robust protection to stop abuse and fraud.
The Arkose Advantage


User-Centric Authentication




Undermine the Economic Drivers Behind Gaming Fraud
Arkose Labs Fraud and Abuse Prevention Platform analyzes data from user sessions to determine the context, behavior, and past reputation of every request. Traffic is classified and triaged based on its risk profile. Suspicious traffic is presented with enforcement challenges that differentiate between true users and fraudsters with certainty. A continuous feedback loop slashes false positives and minimizes the impact on good users.

