Artificial Intelligence (AI)

My feed swings between AI worship and AI scorn. After a year of using these tools hard, here is the unromantic version: a language model is a very good guesser. Once you see prediction and measurement as two different jobs, both the hype and the cynicism fall apart.

Two detectives, same case. The one who guesses fast, or the one who shows her work? Why cybersecurity can't run on lucky guesses — and how empathetic processing bridges the two.

Earlier this week, WitFoo and the University of Canterbury released 100 million labelled cybersecurity records to Hugging Face under Apache 2.0. Here's a three-prompt walk-through for putting it to work in a stack you already own (Grafana, Sentinel, Splunk, Elastic), using Empathetic Processing to translate, detect, and visualise across your own data.

In 2003 at the Naval Postgraduate School, the Blaster worm taught me hard lessons about patch windows, perimeter assumptions, and the laptops that walk in from outside. With Anthropic's release of Mythos, we're about to relive a version of that August, compressed and supercharged. Here's the guidance I gave my team and what I wish I'd known in Monterey.

After publishing The Nuclear Code Fork, smart engineers pushed back with reasonable objections: use mature projects, wait for stable releases, pin your versions, trust the process. Every one of those heuristics was right once. Here's why they're wrong now, with specifics from Log4j, Trivy, LiteLLM, and tj-actions.

We killed all 450 external dependencies in our analytics platform and brought every line of code in house. It took two days, ~305K tokens, and uncovered 14 vulnerabilities that were already sitting in our stack. Here's why we did it, what we found, and what it actually cost.

In November 1998, a terrified young sailor stepped onto the flight deck of the USS Kitty Hawk during night operations and had to make a hard choice under impossible pressure. Twenty-seven years later, the tech industry could use that same lesson.

A new framework for planning projects that blends AI token costs with human labor rates, breaking work into classes of effort where cost, speed and quality can be optimized across every sub-task.

You cannot accelerate a process to supersonic speeds if it barely works at five miles per hour. Applying Anthropic's 4D Framework for AI Fluency to cybersecurity automation reveals that all four D's (Delegation, Description, Discernment, Diligence) fail when built on the same siloed, philosophy-free foundations that have plagued SECOPS for decades. The consequences in our field are uniquely severe: the AI can't go to prison when it gets it wrong.