The Driving Force Behind Creating KasadaIQ
At Kasada, we are all about stopping bots. We search through hundreds of millions of data points daily to understand, identify, and block automated threats for our customers. These data points are the digital fingerprints left each time a user interacts with or touches our customers' infrastructure. It is easy to forget – as the daily count edges ever closer to a billion requests – that there is a human behind each of them, even the automated requests. A bot does not appear in the wild without human involvement. We must understand the humans behind these bots, their motivations, and their communities to predict their next move.
We started with an idea.
Imagine if we could detect attacks on companies that Kasada is not protecting. This idea of maximizing the positive impact Kasada delivers to the world captured our imagination, so we built a system to shine a light into these dark spaces.
We looked around and found no obvious solution; the established threat intelligence industry does not focus on business logic attacks. The Blade Framework, the bot equivalent to the Mitre ATT&CK Framework, categorizes everything neatly – but is not actionable. We knew that our service needed to move “beyond the browser” to detect bot attacks. It needed to know exactly who was being attacked. It needed to discover who was conducting the attack. And it needed to understand the magnitude and impact of the attack.
We needed a system that enabled us to predict the likelihood that a company was – or was about to be – attacked.
We designed KasadaIQ to be that system, consuming millions of signals to help Kasada understand the shape of the botting ecosystems. These signals, taken from all corners of the Internet, provide high-fidelity data that enables Kasada to proactively protect customers of our IQ service.
