Abnormal Blog

Explore how Abnormal's engineering team advances internal development with an AI-driven platform that standardizes infrastructure, reduces setup time, and enables both engineers and AI agents to build and deploy services more efficiently.

Discover how Abnormal AI leverages AI tools like Cursor and Model Context Protocol (MCP) in production to accelerate development.

Discover how Abnormal AI accelerates developer velocity with its secure, in-house Model Context Protocol (MCP), integrating tools like GitHub and Jira directly into local environments to streamline workflows without compromising security.

Learn how Abnormal has employed Cursor to optimize our enterprise codebase for LLMs, automate project rules, and build a security-first AI dev culture.

Learn how Abnormal Security minimizes false positives and false negatives with a multi-layered approach to cyberattack detection and email security.

Explore how operating curves help optimize system performance by visualizing competing metrics, making trade-offs, and achieving efficient resource allocation.

Learn how Abnormal Security leverages large language models (LLMs) to enhance security awareness and automate SOC teams’ workflows with AI Security Mailbox.

Discover how Abnormal Security leverages AI and decision trees to extract signals, analyze context, and detect sophisticated email threats with high accuracy.

Learn how Abnormal Security leverages large language models (LLMs) thoughtfully with safeguards and GenAI-based quality assurance testing.

Discover how Abnormal Security leverages large language models (LLMs) to automate and enhance email threat detection with AI-generated detection rules.

Learn how Abnormal uses natural language processing or NLP to protect organizations from phishing, account takeovers, and more.

We recently shared a look at how the Abnormal engineering team overhauled our Unwanted Mail service architecture to accommodate our rapid growth. Today, we’re diving into how the team migrated traffic to the new architecture—with zero downtime.

In episode 11 of Abnormal Engineering Stories, David Hagar, Director of Engineering and Abnormal Head of UK Engineering, continues his conversation with Zehan Wang, co-founder of Magic Pony.

As our customer base has expanded, so has the volume of emails our system processes. Here’s how we overcame scaling challenges with one service in particular.

In episode 10 of Abnormal Engineering Stories, David Hagar, Director of Engineering and Abnormal Head of UK Engineering, sits down with Zehan Wang, co-founder of Magic Pony.

Learn how explicitly modeling dependencies in a machine learning pipeline can vastly reduce its complexity and make it behave like a tower of Legos: easy to change, and hard to break.

In episode 9 of Abnormal Engineering Stories, Dan sits down with Mukund Narasimhan to discuss his perspective on productionizing machine learning.

As we’ve scaled our customer base, the size of our datasets has also grown. With our rapid expansion, we were on track to hit the data storage limit of our Redis server in two months, so we needed to figure out a way to scale beyond this—and fast!

At Abnormal, we pride ourselves on our excellent machine learning engineering team. Here are some patterns we use to distinguish between effective and ineffective ML engineers.

In episode 8 of Abnormal Engineering Stories, Kevin interviews Saminda Wijegunawardena, an engineering leader who is no stranger to fast-growing enterprise startups.

There are many approaches to ensuring our system can adapt quickly to new attack trends. One of the most successful approaches we’ve found is to take in the newest attacks and retrain our system end-to-end to detect them.

Here at Abnormal, our machine learning models help us spot trends and abnormalities in customer data in order to catch and prevent cyberattacks.

Abnormal's fundamental job is to detect malicious emails like phishing and business email compromise attacks and other malicious events, such as suspicious sign-ins that indicate an account has been hacked.

Tim Tully, Partner at Menlo Ventures, grew up in Silicon Valley, where a love for coding was kindled in him. Tim is a technologist to the core, which innately led him to become an elite technical leader at companies like Splunk and Yahoo.