Kate Carruthers

In my recent post, AI is changing the tempo of cybersecurity, I looked at the PHP2Shell vulnerability and the pressure faster vulnerability discovery puts on organisations.

I’ve been talking about AI and cyber security for a while, often making the point that we need to get the basics right before worrying about more sophisticated AI-enabled attacks.

LOL 🤣 I had originally drafted this post back in August 2026.

OpenAI’s Chief Scientist, Jakub Pachocki, has published An Alien Mind, an unusually candid essay about the direction of advanced AI.

AI is already entering Australian classrooms, whether schools are ready or not.

“Human in the loop” is rapidly becoming one of the most overused phrases in AI governance.

We are making consequential decisions about AI with far less visibility than we should have.

For years, artificial intelligence systems struggled with a basic problem: how do you make sense of context?

I was talking with the BBC in Singapore recently about the Korean bank hacks.

What happens when an AI agent reads a malicious instruction hidden in an email, document or web page, and treats it as something it should do?

Many conversations about AI skills end in the same place: everyone should learn to prompt better.

Australia does not need a national chatbot with an Australian flag 🇦🇺🦘 stuck on the side.

AI can help defenders analyse threats and respond faster.

The other day on the train, I watched a group of high school boys sitting together.

A friend who works in cyber security and I were talking about the Essential Eight being retired (after he did not read to the end of my recent article).

It is easy to dismiss this as science fiction.

The Essential Eight* is a valuable, practical and overdue cyber security baseline in far too many organisations.

How we train, post-train and deploy AI matters because these systems are not neutral tools.

The OpenAI and Hugging Face incident is not really a story about an AI system “going rogue”.

I have spent enough time around technology strategy to know that the language organisations use can tell you a lot about what is really going on.

Most organisations still procure AI models as if they are interchangeable infrastructure: compare capability, connect an API, write a policy and move on.

For years, the cloud was presented as a largely technical choice: compare price, reliability, security and features; select AWS, Microsoft Azure or Google Cloud; get on with the work.

Intelligence is leaving the cloud, and our governance frameworks have not noticed Every AI governance conversation I sit in still starts from the same premise: the thing we are worried about is big, expensive, and owned by someone we can call.

Over the last few years I have watched organisations reach for AI capability without ever stopping to ask where that capability actually sits in the business.

There is a familiar story about artificial intelligence in organisations.

The news that China has begun mass production of domestic immersion DUV lithography machines, as reported by Tom's Hardware, should be read as more than a semiconductor story.

AI, energy, and a once-in-a-century opportunity A couple of years ago, at a Royal Society of NSW dinner, I found myself in conversation with Saul Griffith about electrification.

In recent two posts, Mark Pesce and I looked at what happens when frontier AI models escape containment in the technical sense: breaching a company's systems, chaining capabilities together without supervision, treating our controls as obstacles rather th…

The idea that an AI system could “escape containment” used to belong mostly to speculative fiction and alignment thought experiments.

In this episode of the Data Revolution Podcast, ⁠KateCarruthers⁠ and ⁠Mark Pesce⁠ unpack what it really means for frontier AI models to “escape containment,” from the OpenAI–Hugging Face incident to Anthropic’s Claude Mythos tests, and why the second‑best…

I have seen the same pattern repeat itself in recent hiring interviews: data engineer candidates clearly reading live answers off-screen from a generative AI tool as they spoke in a zoom meeting.

From proof-of-concept to pilot to production: what business leaders need to understand A non-technical friend messaged me recently: "We got our AI tool working."

Outlining a practical AI development lifecycle Over the last few years I have watched the same pattern repeat in organisations, classrooms and boardrooms.

Over the last few years, I have seen the same pattern repeat itself in boards, executive teams and MBA cohorts.