A Swedish startup called Eggsplain spent the spring working with the country's privacy regulator on a question that has become surprisingly difficult to answer. When an AI supplier fine-tunes a model using personal data, who is actually responsible for that data?
Last week I argued that much of what is being marketed as agentic AI in GRC is not actually agentic. The market response was interesting because very few people challenged the core premise. Most practitioners already sense that something is off. They sit through the demonstrations and hear the language. They watch the AI summarize documents, answer questions, generate narratives, and produce recommendations. Then they leave wondering whether they just witnessed the future of GRC or a very polished presentation wrapped around capabilities that have existed in various forms for years.
More than 80% of the code merged into Anthropic's production codebase is now authored by Claude. The statistic appears almost casually in a lengthy report published this week by the Anthropic Institute. It arrives alongside benchmark results, productivity measurements, engineering data, and speculation about recursive self-improvement. Yet it is arguably the most important number in the document because it describes something that has already happened rather than something that might happen next.
One line in the Malta Financial Services Authority's latest AI guidance says more than the rest of the document put together. The regulator reminds firms that artificial intelligence does not change the objectives of financial regulation. The statement appears almost in passing, but it captures a problem regulators across Europe are beginning to see. AI is arriving inside financial institutions wrapped in promises of efficiency, automation and better decision-making. What it has not brought with it is any exemption from accountability.
The White House recently issued a sweeping executive order aimed at strengthening U.S. cybersecurity capabilities through advanced artificial intelligence while deepening cooperation between the federal government, critical infrastructure operators, and AI developers.
There is a peculiar imbalance taking shape inside many organizations. Over the past two years, companies have assembled AI governance committees, drafted acceptable-use policies, updated risk registers, and launched internal working groups dedicated to understanding the implications of artificial intelligence. Compliance teams have studied emerging regulations. Privacy officers have debated data-sharing restrictions. Boards have asked increasingly pointed questions about oversight, accountability, and risk.
For much of the debate surrounding the EU AI Act, "high-risk" has been treated as a category everyone understood. It turns out that understanding it and applying it are not quite the same thing.