Fraud Prevention in the Age of AI Agents

A major challenge for almost every online service is reliably identifying users and knowing whether a given user or agent can be trusted. Historically, web services leaned on cookies stored in the browser to remember a visitor and recognize them when they return. The problem is that cookies live in the user's own browser and are easy to remove, block, or avoid entirely, so a bad actor can simply clear them to escape recognition. IP blocking and captcha puzzles are firmer approaches, but they can frustrate legitimate users while doing little to stop a determined attacker. That leaves developers stuck between being too lax in losing control or too strict and driving real users away. Fingerprint JS was started in 2012 as an open source project. In 2019, Fingerprint was launched as a separate full commercial platform built on the original Fingerprint JS GitHub library to recognize devices and browsers without relying on cookies. It handles identification by reading over 100 signals from fonts and screen resolution to how a device renders audio and graphics. Keisha Rose works in developer relations at Fingerprint. In this episode, she joins Gregor Van to talk about fingerprinting, how fingerprint combines multiple signals to identify fraudulent actors, how to distinguish legitimate AI agent use from anonymous or malicious bots, and how fingerprint's research team uncovered and disclosed a privacy vulnerability in Firefox and Tor that could have been used to track people across the web. Gregor Vand is a security focused technologist, having previously been a CTO across cybersecurity, cyber insurance, and general software engineering companies. He is based in Singapore and can be found via his profile at van.hk or on LinkedIn. Hello, and welcome to Software Engineering Daily. My guest today is Keisha Rose from Fingerprints. Welcome Keisha. Hey, thanks for having me. Yeah, very excited to have you on today. We're gonna be hearing about a product and a technology, I guess, that runs on a lot of people's browsers and they're kind of using it without them even realizing in a positive way. We're gonna get into all that in just a second. I'd love to just hear what was your road into technology in general and then what was your path to fingerprint? Yeah, so I actually started out as an analyst in behavioral health in a nonprofit and I ended up making my way into tech because I fell in love with one of the companies I ended up working for which is Tableau Software. Shout out to Tableau. But that's what kind of got me into it. I started there as a sales engineer. I then moved over to the product side as a PM. I was doing a little bit of DevRel at the same time and I left Tableau to go to Algolia as a PM and I was like, you know, I kind of miss that DevRel side. So in my next role, was like, let me look for something a little bit more …