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Integrity is key in today’s hyper connected web-based application world. From social media to financial services, site and application integrity is critical to a platform’s success. Yet, automation is helping the bad guys as well as the good. Sites today are under attack from those who would steal content through automated screen scraping, artificially drive reputation, or leverage stolen credentials to compromise an account to engage in fraudulent activity.
With nearly 1/3 of all internet traffic being generated by malicious, automated bots, it’s more important than ever to be able to separate human from bot, malicious from suspicious, to ultimately get in front of and block bot attacks.
Mitigating bot attacks used to mean modifying your apps, but even that didn’t protect your APIs, and frankly continued protection requires something more dynamic in nature.
In this podcast I’m joined by Larry Link & Shreyans Mehta from Cequence, a silicon valley startup who is applying real-time network analysis, machine learning, threat intelligence, and behavioral analytics to accurately detect and mitigate bot attacks without affecting legitimate user traffic.
Since web, mobile, and API application service attacks don’t involve malicious content, it’s often not possible to rely on traditional security tools. Larry and Shreyans help us understand how Cequence focuses its attention on detecting the underlying behavior and intent of each application request, thereby securing modern application infrastructures.