Is Your AI Model Going Offline a Risk?
Your preferred language model going offline is not a new risk. But now that you are automating things and using agents, your approach to this risk may need rethinking.
Your preferred language model going offline is not a new risk. But now that you are automating things and using agents, your approach to this risk may need rethinking.
There’s lots of talk about AI risks right now. Finally. But the reality in your own businesses won’t change. I recommend you embrace the opportunity, with some simple advice.
‘Human-in-the-Loop’ checks have some serious failings. Here is a practical, non-technical checklist to use when managing AI agents.
The NCSC published interim advice on managing the security risk of AI agents. Read it cold and you would reasonably conclude that agentic AI is only for organisations with a dedicated security team. That’s not the case; but read this first!
A simplified safety checklist for anyone using AI skills
What do we mean by AI transparency? In the definitions emerging from policy, it is too narrow and we need to start thinking about transparency more broadly. This post is the second of three about practical AI governance, continuing from this first post.
There is a flood of AI policy masquerading as governance, and it does not survive contact with an actual AI agent. If you want to automate a process, you first have to understand that process, including the messy bits nobody wrote down. Stafford Beer worked out why this matters back in the 1970s, and his insight is uncomfortably relevant now: your controls have to be as complex as the thing they are controlling. A prompt and some context will not do it; practical governance starts with feedback loops and safe experimentation, not documents.
There is an old framework for understanding how expertise is built. It predates the modern workplace by centuries, but it maps onto professional development with precision. And right now, AI is breaking it in ways most organisations have not even begun to notice.