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I've been working on an open source ZTNA project/product https://github.com/octelium/octelium for many years and I am actually very open to the idea. Machine learning has already been in use for years when it comes to anomaly detection. Maybe modern LLMs can be used to automate access control, partially or fully at enterprise scale in the future. The idea is to have a policy engine that's controlled by AI that uses a mix of long-term semi-static info such as group memberships and permission boundaries, short-term info such as behavior (e.g. access logs) and current session's authentication strength level (AAL) such as FIDO as well as near real-time external information (e.g. IdP, SIEM, threat intelligence, on-call management) to dynamically adjust users' access control to build up some sort of a context for each user and each session that can increase or decrease permissions without having to manually add policy rules or going through request/approval flows, while optimizing for the main objective (i.e. minimizing access grants for every subject to any resource under whatever context unless when it's necessary). This can be unified for both humans and non-human identities, including agents.
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Good, realistic take. There's a lot of context that can be drawn on to make better decisions and look for weird.

And then agents come along and some people want their agent to be able to do anything they can do with zero friction. That usually lasts until the first time something goes wrong ;)

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Actually zero trust can be seen as an optimization problem, you almost certainly want to globally deny every single request, even for authenticated users under all circumstances to all resources, unless when "necessary", "necessary" here is the key because it needs to be as dynamic and as fine-grained as possible, context-based and scalable. For small businesses, RBAC and ACL rules can be fine, but for enterprises with thousands or tens of thousands of employees, contractors and now AI agents which need varying permissions to different resources at different times, you would have to define thousands or tens of thousands of rules that might conflict with one another and are hard to maintain. ABAC and policy-as-code can mitigate to a certain extent providing dynamic context-based fine-grained access control. But if you could gather as much information as possible about every user and every session, combine it with some boundary permissions and static rules, and feed it periodically to some reasoning engine that is tasked with dynamically increasing/decreasing permissions for each session based on all such info, you can keep your manual/static access control policies to a minimum and maybe even eliminate JIT in some cases. The real question is whether modern LLMs and transformers are the correct and optimal architecture for this problem in the first place.
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+1000, and the role of non-deterministic security is to compose deterministic security primitives.

Deterministic executes fast and you know what it will do. You want to evolve your ground truth, not hope for the best.

That said, there is a role for probabilistic elements in the security model...as bait and signal.

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I don't think the point here is to fully replace deterministic with non-deterministic-based access control. The point is to combine the traditional manual/coarse-grained deterministic access control with an additional layer of dynamic, fine-grained non-deterministic access control.
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I completely agree.

I think LLMs may have a role in security posture, specifically flagging/identifying potential threats for human review. But a Zero Trust/Access Controls should be a HARD boundary, not an inconsistent one.

The problem we have right now is that there are some legitimately interesting ideas out there for things we could be using LLMs for, but we also have a ton of "I have a hammer, and everything looks like a nail" going on too.

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> The problem we have right now is that there are some legitimately interesting ideas out there for things we could be using LLMs for, but we also have a ton of "I have a hammer, and everything looks like a nail" going on too.

It's not a problem as much as a phase the world is going through. LLMs are a technological breakthrough in the same generality class as the Internet, or possibly electricity, and in both cases the world went through a phase of attempting to apply the newfound invention to literally everything. It's a necessary phase, when a technology obviously could be useful for everything, but it's not obvious whether it will in practice.

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I don't think Google are advocating for removing the deterministic controls, are they?

Sounds like adding the LLM in would be on top of the existing deterministic controls.

Existing controls handle the "Should you be able to access this resource right now?" the LLM handles "Is this user behaving as we expect them to while accessing this resource?"

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Agreed; I don't think we want security controls to devolve into an argument between LLMs. Attacking and defending LLMs operate in an ecosystem, and are (hopefully) limited by constraints. Defending LLMs will be able to rely on and evolve those constraints. Attacking LLMs want to find a way around them.
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Who said anything about an LLM doing it? Credit card companies have been using AI quite well for fraud detection for years before LLMs were released to the public.
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From Google point of view, their users are nothing more than a statistic. An hacked account is a statistic. No surprise they see software that way.
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