Arkose Labs
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Arkose Labs protects account integrity with adaptive defenses that stop bots, AI agents, and human fraud networks across the user journey.
Summary
Arkose Labs is a commercial platform that analyzes and controls traffic patterns to stop bots, AI agent fraud, account takeovers, and scraping. It is available for evaluation and operates as a service, making it suitable for platform engineers concerned with web-app-security. Its documentation positions it alongside fraud prevention solutions, and the product launched in 2015.
Arkose Labs protects account integrity with adaptive defenses that stop bots, AI agents, and human fraud networks across the user journey.
How Arkose Labs helps
Which requests hitting my login and signup endpoints are bots instead of real users?
scores each request's traffic pattern and behavior against known bot signatures and flags automated attempts before they reach the account
Which of my accounts are being targeted by credential stuffing or takeover attempts right now?
watches authentication traffic for patterns matching known takeover techniques and blocks the session before the credential check succeeds
Is the traffic hammering my site actually an AI agent instead of a human or a simple script?
inspects request behavior for the signatures of autonomous agents specifically, distinguishing them from both human users and traditional bots
Is something scraping my catalog or content pages faster than a person could browse them?
flags request sequences whose pacing and coverage match automated scraping rather than normal browsing and can stop the session mid-crawl
Which of my traffic decisions turned out wrong, and what does that cost me?
backs its verdicts with a warranty of up to one million dollars, giving a concrete financial stake in the accuracy of its own calls
Can I tell whether a suspicious signup or login was a bot before deciding whether to challenge it?
runs the traffic-pattern analysis first and only escalates to a challenge step for sessions it can't already classify with confidence
Languages
Release-note detail over time
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Releases
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