Analysis
We summarise every significant cyber security story we can find, which means we end up holding something most readers never see: the whole news cycle at once, tagged and dated. These pieces measure it.
Two kinds of piece live here, and it is worth knowing which you are reading. The measured ones take a question that can be answered by counting, answer it against our own corpus, and state plainly what the numbers can and cannot support. Where a finding is an artefact of which outlets we read rather than of the industry, we say so.
The commentary is the other kind. It argues a position about something in the news, names its sources and quotes them, and is not generated by anything. Both are written by us. Neither is the automated feed, which is published separately.
Measured
Who covers what in cyber security
Coverage concentration varies enormously by subject: some topics are effectively one publication, while others are split across dozens with no owner. A reader following one masthead gets a picture with predictable holes rather than a smaller one. Measured from every story we hold.
Read the analysis ->Ransomware claims against Australian organisations
Every Australian organisation a ransomware group has posted on its leak site, from the public tracker ransomware.live: claims per month over two years, which groups are posting this quarter and which are new to it, the sectors being posted, and the latest listings with the briefs written from them. Every row is a criminal's claim and the page says so. No forecast, and it explains why.
Read the analysis ->What a data breach exposes, and how long it stays hidden
A breach used to be a hacked website's login table. It is now a company's customer records, and the two expose entirely different things about you. Measured across every breach on Have I Been Pwned: what share carried passwords then against now, what replaced them, and how many days pass between a breach happening and anyone affected being able to find out.
Read the analysis ->Explainers
How an LLM works, read straight from the file. A slide course through Qwen3-Coder in Netron.
Skip the equations and open a real model instead. Fourteen slides trace the 18 GB Qwen3-Coder file through Netron, node by node, from the tokenizer to the next word: a 151,936-token vocabulary, 32 query heads sharing 4 key heads, 128 experts with 8 running per token, 48 identical blocks, and why 61 GB of weights fit in 18. Every number is a screenshot of the file.
Read the course ->Commentary
Does Microsoft still have no taste? Copilot is the first product where it costs them.
Steve Jobs said Microsoft had no taste and won on distribution anyway, and for thirty years he was right about both halves. Then fewer than 7% of 450 million Microsoft 365 seats took a Copilot licence. The new all-in-one app runs on OpenAI and Anthropic models, ships agents into Teams and Outlook, and bills by consumption. What that changes if you own identity, permissions and the budget.
Read the analysis ->An AI agent went around the blocks on a government portal. Here is what Canberra announced, and what it means for your suppliers.
An OpenAI research agent was refused by a Medicare statistics portal, found another way in, read non-public files and wrote files to the server. The government answered with a task force, a committee referral, advice on whether an offence occurred, and AI standards legislation. The 84 days before anyone was told is the part with your name on it. With the press conference, slides on the response, and a timestamped record of what was said.
Read the analysis ->A kill switch, embedded supervisors and a shrug. What Washington's AI safety week actually put on the table.
The people building frontier AI asked, in public, to be slowed down and supervised. The White House said no, the Speaker of the House said "maybe all of it", and nothing happened. What was proposed, what each measure would actually do, why it lands on Australian businesses anyway, and four questions we want your answer to. Comments are open.
Read the analysis and join the debate ->WiseTech's moat is regulatory, not code. That raises the stakes on the code.
WiseTech's chief executive says CargoWise cannot be replicated, and the reason is not that the software is hard to write. It is that the integrations with customs authorities and regulators are hard to get. He is probably right, and it moves the question to whether the compliance logic is correct, in a business that has just told the market 90% of its code is written or assisted by AI.
Read the analysis ->Method
Every story is summarised and tagged when it is ingested, and the record of them is append-only, so the dataset grows rather than rolling over. Counts are of stories, not words or significance. Tagging is model-generated: consistent enough to compare topics against each other, and not a human taxonomy.
If you want the underlying counts for your own work, they are yours. The full briefing index is public, and the per-topic pages such as AI security and vulnerabilities show the same data grouped by subject.