I'm looking to understand how Daybreak, GPT 5.5 and Claude Mythos might change cyber defense: where they can deliver real-world security capability, where expectations may be ahead of what's technically feasible, and where they risk introducing new uncertainty or overreliance. 1. Based on what you've seen, is there a use case where these models would let defenders do something they can't realistically do today? Not just faster, but differently? What would that look like in practice? 2. A couple of analyses of the models suggest strong reasoning over code, configs and attack paths. How much of security work depends on context that isn't captured in those artifacts? Where do you expect that gap to matter most? 3. Some findings show these systems can persist through multi-step tasks, including exploitation, chaining actions and adapting to feedback. In a real environment, what could interrupt that, and how much does that limit what the models can actually achieve? 4. Given that much of the demonstrated capability is around chaining attacks or adapting techniques, where do you think that translates more directly for attackers than for defenders? What's a concrete example of that imbalance? 5. Benchmark-style tests focus on whether a model can complete a task, but in real security work, how it fails often matters more. What's a failure mode you'd expect in practice that wouldn't show up in benchmarking? 6. If these models are good at producing coherent, step-by-step explanations, where do you see the risk of analysts trusting that narrative too much? What kind of situation would reveal that weakness? 7. Based on what you've seen so far, what additional evidence would you need to confidently say these systems are ready to improve security outcomes?