Future of ML Platform w/ Jeshua Bratman & Nico Koumchatzsky Over the last three years building our ML-based cybersecurity products at Abnormal Security, I’ve benefitted enormously from discussions with colleagues in the ML space. This podcast aims to make some of those conversations available. In our second episode of Abnormal Engineering Stories, Nico Koumchatzky and I discuss the future of…

It was a pleasure sitting down with Tim Shi of Cresta and Saam Motamedi of Greylock to discuss building companies around machine learning technology! It’s one thing to add machine learning and artificial intelligence to existing software, but it’s quite another to build a company with ML at its core…

Analysis of ML engineering lifecycles, common pitfalls, and a copy-and-paste template you can use. — Machine learning engineering is hard, especially when developing products at high velocity (as is the case for us at Abnormal Security). Typical software engineering lifecycles often fail when developing ML systems. How often have you, or someone on your team, fallen into the endless ML experimentation twiddling paralysis? Found ML…

Developing a machine learning product for cybersecurity comes with unique challenges. For a bit of background, Abnormal Security’s products prevent email attacks (think phishing, business email compromise, malware, etc.) and also identify accounts that have been taken over. …

At the core of all Abnormal’s detection products sits a sophisticated web of prediction models. For any of these models to function we need deep and thoughtfully engineered features, careful modeling of sub-problems, and the ability to join data from a set of databases. For example, one type of email…

Authors: Jeshua Bratman and Vineet Edupuganti Our core email attack detection product at Abnormal works by processing each incoming message, applying a series of classification models, and ultimately deciding if a message might be an attack. …

My favorite part of working at Abnormal Security is seeing the myriad of nefarious attacks we are able to stop. These attacks include everything from attempts to steal millions of dollars, to installing ransomware crippling hospitals, to state actors compromising our power grid. And — right the core of the…

This article is a follow-up to one I wrote a year ago — Lessons from building AI to Stop Cyberattacks — in which I discussed the overall problem of detecting social engineering attacks using ML techniques and our general solution at Abnormal. …

On March 19th 2016 John Podesta, was tricked into revealing his Gmail credentials to a Russian-backed organization who then released emails regarding the Clinton campaign, effectively influencing the 2016 election. Social engineering attacks like this (and much more sophisticated ones) are on the rise and getting more advanced every day…