AISB 2026

tl;dr: Provides tons of resources (training, data, connections) for work on AI safety specifically (e.g. changing jobs, founding a company, furthering existing work); strongly recommended if that is your interest. Elsewhere in AI security, you’ll see more value the closer your work is to directly securing frontier labs. Definitely Effective Altruist-tinted, focusing on longer-term risks over short-term ones: there’s upward path for some enterprise challenges (e.g. AI control), but those connections are outside the curriculum. The lectures were consistently good, and guest lectures outstanding, but hands-on exercises were of mixed quality: good conceptually, some exercises exemplary, but others testing library usage rather than the content, which I expect is improved for the next iteration. Personally, discussion with other attendees – a truly exceptional cohort – was as valuable as the curriculum itself, and more directly applicable to my work. Overall, it was worth my time. Applications for the next iteration in Las Vegas 2nd–8th August are open until 21st June. I actively encourage you to reach out to discuss any aspect of AISB in more detail!

In April, I spent a week at AI Security Bootcamp 2026 Singapore alongside 15 other cybersecurity professionals, learning about challenges around securing increasingly-capable AI systems. I did detailed writeups for Day 0-1 and Day 2, though I fell behind in favour of seeing Singapore and socialising with other attendees (with a particular shoutout to Mattia).

I promised on LinkedIn beforehand to give insight into “how a room of cybersecurity experts tackle securing frontier AI systems”. So how did we do it?

As outlined on day 1, this week I’m in Singapore for AI Security Bootcamp 2026. This is an informal daily update written at the time: I followed up with a more formal writeup and takeaways a month later.

On the second day, we had our first guest lecture – on how Zero-Knowledge Proofs can be used to guarantee e.g. output was produced by the correct model, based on only external input – before a lecture and exercise on agents and attacks against them (prompt injection, tools, MCP, RAG), and after lunch a lecture and exercise on how we can safely make use of models that we know misbehave.

Core takeaways:

• Zero-Knowledge Proof allows us to prove only expected models are used on only expected data, among other security properties. The computational overhead is currently prohibitive, but by applying some workload-specific tricks there’s hope this overhead can be brought down.
• The attack surface of coding agents is huge: anything you have read access to could be used to attack the agent, and anything you have write access to could be used to achieve persistence. You should run with robust oversight, in a sandbox, or both.
• After my own reflection: control techniques feel relevant even before ASI. Research has largely looked at toy examples where the untrusted model produces code that might fail in only one input case, but the setup could transfer to any case where an AI system can take out a harmful action. Claude Code’s auto mode strongly resembles the trusted monitoring setup with a more concrete failure case!

tl;dr: first of 7 2 daily posts (Day 2) from AI Security Bootcamp with 15 other cybersecurity professionals; today set the stage with a survey of AI safety topics and challenges at today’s frontier. Takeaways: securing AI systems is one part of making them safe; cybersecurity mindset applies across safety challenges

As I posted on LinkedIn, this week I’m in Singapore for AI Security Bootcamp 2026. I’ll do informal daily posts – like this one – and follow up with a more formal writeup and takeaways after the week is over. If you want a true tl;dr, feel free to wait for that post to drop please check out that post!

On the zeroth and first days, I met with friends old and new to settle in and set the stage for the rest of the week: surveying the problem space and where we are now (including a tour of the Claude Mythos Preview System Card), and reinforcing understanding of the AI safety landscape.