I'm sure language models and transformer techniques will be (or more likely: already are) an important part of the contemporary systems that do this stuff. But I'm skeptical that they care much about GPT-4 itself (or other general models).
I'm not skeptical about whether they think it is useful and an important capability to incorporate ML techniques into their systems, I'm unsure how much utility they see in general (the "G" in AGI) models.
How? I'm not trying to be combative, I genuinely am curious if you have an idea how these things could be usefully applied to that problem. In my experience working in the information security space, approximate techniques (neural nets, etc.) haven't gotten much traction. Deterministic detection rules are how we approach the problem of finding the needle in the hay pile. So if you have a concrete idea here that could represent an advancement in this field.
In a world where false negatives--i.e. failing to detect a sharp needle--are the worst possible failure mode, approximations need to be handled with exceeding care.
But for all we know intelligence services could be using LLMs for years now, since they are usually a few years ahead of everybody else in many regards :-)