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All security measures "simply shift the attack vector." The idea is to shift it to something that is more difficult to compromise.

This is a layer on top of normal security, so this isn't a matter of compromising the "brain" instead of RBAC, etc, but in addition to.

Attribute-based access control is already a thing. User X logs in the US East between the hours of 6am and 6pm. If User X logs in from Russia at 3am, deny access. This seems like an evolution of that pattern

Before zero trust, once you logged in, you have access to the entire kingdom of resources/files/APIs etc.

To me, this sounds like zero trust version 2.0. The "brain" challenges agentic AI trying to access resources it normally doesn't access.

Personally, I like the name "Beyond Zero" because it isn't oxymoronic like "Zero Trust".

these are just fluff words, not a revolutionary change.

zero trust helped a lot because it forced vendors to stop relying on people magically doing the right thing ina centralized system. it decentralized boundaries.

this paper just says you still need deterministic boundaries and access control, not just have an llm say safe or unsafe. cool thanks google haha.

ps: hi guys, i know yall reading. sometimes its not the concept, its the implementation. else active shield or whatever would have worked too. not everyone can be the T7-9 thinker if implementing is seen as "for peasants"

Google already has a system where data and access control are combined as part of shared docs. I think Zanzibar for docs themselves on GDrive. Not sure the name of the system that manages a portion of a document in a shared doc which colocates data and access data. This seems like a next generation of that?

One partial answer is to not make it a single brain. If the reasoning layer that evaluates a request is independently audited by a second pass at lower temperature with an explicit skepticism mandate, compromising the evaluator requires compromising both passes simultaneously — and they're running the same local model with different system prompts and no shared state between them. Doesn't eliminate the attack surface but raises the cost considerably compared to a single inference gate.

> Preparing for AI agents means expanding auditability and reversibility in software

While I think that makes sense, I also think more controls are a good solution. That is, prevent access to the sales data without escalation, probably to a human, but possibly to another AI. The issue there is twofold:

- if you start with least privilege, the agents become less useful

- need to balance escalation with frequency otherwise it's just another version of MFA fatigue

Agree that auditability is important because otherwise you don't know what you don't know.

not sure if you made the app or not - I like it a lot, only feedback is it should be a bit more intuitive on how to start the slides, I couldn't figure it out and old ppl like me don't always know

The font is different after downloading from the preview on the web page, but I still like it.

This would be paired with deterministic controls. So you have, for example, "engineers can only read from the database, and only after they've perform a 2FA" but then there's context like "why is this engineer reading from the payments table when their IP is in a weird location and they're supposed to be on PTO?" and perhaps that's something a model decides.

It's possible to turn that second thing into a sort of "risk score" but it's very hard and a model is going to potentially be better at it.

Just to make my point: can I really trust humans to keep up with the required identity data that will give me enough "credibility" so the AI will give me access?

Let's say I had a promotion, who changes my title in the system, who changes my responsibilities and my place in the org chart, more importantly, will they do it or is <HR_NAME_HERE> on leave and forgot? those are data points required by the agent to determine if I'm "good enough" to access a certain resource.

What if... someone spoofed my address and did a flood in one of the resources that are lateral to what I'm allowed to access (let's say I don't have access to company sales but I do to department sales and the attacker floods company sales with requests under my address), would the AI determine that I'm a high-threat actor and not allow me to access legitimate files going forward?

Will exceptions be made by humans? In which case we go back to human-managed permissions.

Sorry, I might be barking up the wrong tree but I think these are questions that are not meant to be solved during implementation. And they add to what @firasd said about legitimate-but-odd behaviour

Having a non-deterministic network device in path sounds fun for troubleshooting. AI firewall team about to become the new ticket sponge in every org.

I feel some systems already operate like this, but typically it was done for fraud and abuse, now it will be done for permissions as well.

It does sound like something that could be easily exploited. Hello HAL, I am martha from accounting.

Was this what Wiz was doing?

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.

+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.

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.

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.

This trope is so tiring.

There is a massive difference between Google for enterprise customers and Google for consumers.

The consumer offering is massively subsidized by ads and will use your data for ad placement, although they still never sell your data because that would hurt their business.

The Enterprise offering guarantees you contractually that they never touch your data.

Where do you see them offering this service to you? They’re just publishing what they developed internally, which is what they often do.

Now, Cloudflare, on the other hand, would be much more likely to offer a service like this.